System and method for image segmentation, bone model generation and modification, and surgical planning

ABSTRACT

A computer-implemented method of preoperatively planning a surgical procedure on a knee of a patient including determining femoral condyle vectors and tibial plateau vectors based on image data of the knee, the femoral condyle vectors and the tibial plateau vectors corresponding to motion vectors of the femoral condyles and the tibial plateau as they move relative to each other. The method may also include modifying a bone model representative of at least one of the femur and the tibia into a modified bone model based on the femoral condyle vectors and the tibial plateau vectors. And the method may further include determining coordinate locations for a resection of the modified bone model.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. application Ser. No.16/865,998 filed May 4, 2020, which application is a continuation ofU.S. application Ser. No. 16/376,362 filed Apr. 5, 2019, now U.S. Pat.No. 10,687,856, which application is a continuation-in-part applicationof U.S. patent application Ser. No. 16/229,997 filed Dec. 21, 2018, nowU.S. Pat. No. 10,675,063, which is a continuation application of U.S.application Ser. No. 15/581,974 filed Apr. 28, 2017, now U.S. Pat. No.10,159,513, which application is a continuation of U.S. application Ser.No. 14/946,106 filed Nov. 19, 2015, now U.S. Pat. No. 9,687,259, whichapplication is a continuation of U.S. application Ser. No. 13/731,697filed Dec. 31, 2012, now U.S. Pat. No. 9,208,263, which application is acontinuation of U.S. application Ser. No. 13/374,960 filed Jan. 25,2012, now U.S. Pat. No. 8,532,361, which application is a continuationof U.S. patent application Ser. No. 13/066,568, filed Apr. 18, 2011, nowU.S. Pat. No. 8,160,345, which application is a continuation-in-partapplication of U.S. patent application Ser. No. 12/386,105 filed Apr.14, 2009, now U.S. Pat. No. 8,311,306. U.S. application Ser. No.12/386,105 claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Patent Application No. 61/126,102, entitled “System andMethod For Image Segmentation in Generating Computer Models of a Jointto Undergo Arthroplasty” filed on Apr. 30, 2008.

Application Ser. No. 16/376,362 is also a continuation-in-part of U.S.patent application Ser. No. 15/477,952 filed Apr. 3, 2017, now U.S. Pat.No. 10,251,707, which is a continuation application of U.S. applicationSer. No. 13/923,093 filed Jun. 20, 2013, now U.S. Pat. No. 9,646,113,which application is a divisional application of U.S. application Ser.No. 12/111,924 filed Apr. 29, 2008, now U.S. Pat. No. 8,480,679.

Application Ser. No. 16/376,362 is also a continuation-in-part of U.S.patent application Ser. No. 16/211,735, filed Dec. 6, 2018, which is acontinuation of U.S. application Ser. No. 15/167,710 filed May 27, 2016,now U.S. Pat. No. 10,182,870, which application is acontinuation-in-part of U.S. application Ser. No. 14/084,255 filed Nov.19, 2013, now U.S. Pat. No. 9,782,226, which application is acontinuation of U.S. application Ser. No. 13/086,275 (“the '275application”), filed Apr. 13, 2011, and titled “Preoperatively Planningan Arthroplasty Procedure and Generating a Corresponding PatientSpecific Arthroplasty Resection Guide,” now U.S. Pat. No. 8,617,171. The'275 application is a continuation-in-part (“CIP”) of U.S. patentapplication Ser. No. 12/760,388 (“the '388 application”), filed Apr. 14,2010, now U.S. Pat. No. 8,737,700. The '388 application is a CIPapplication of U.S. patent application Ser. No. 12/563,809 (“the '809application), filed Sep. 21, 2009, and titled “Arthroplasty System andRelated Methods,” now U.S. Pat. No. 8,545,509, which claims priority toU.S. patent application 61/102,692 (“the '692 application”), filed Oct.3, 2008, and titled “Arthroplasty System and Related Methods.” The '388application is also a CIP application of U.S. patent application Ser.No. 12/546,545 (“the 545 application”), filed Aug. 24, 2009, and titled“Arthroplasty System and Related Methods,” now U.S. Pat. No. 8,715,291,which claims priority to the '692 application. The '809 application isalso a CIP application of U.S. patent application Ser. No. 12/111,924(“the '924 application”), filed Apr. 29, 2008, and titled “Generation ofa Computerized Bone Model Representative of a Pre-Degenerated State andUseable in the Design and Manufacture of Arthroplasty Devices,” now U.S.Pat. No. 8,480,679. The '545 application is also a CIP application ofU.S. patent application Ser. No. 11/959,344 (“the '344 application),filed Dec. 18, 2007, and titled “System and Method for ManufacturingArthroplasty Jigs,” now U.S. Pat. No. 8,221,430. The '809 application isa CIP application of U.S. patent application Ser. No. 12/505,056 (“the'056 application”), filed Jul. 17, 2009, and titled “System and Methodfor Manufacturing Arthroplasty Jigs Having Improved Mating Accuracy,”now U.S. Pat. No. 8,777,875. The '056 application claims priority toU.S. patent application 61/083,053, filed Jul. 23, 2008, and titled“System and Method for Manufacturing Arthroplasty Jigs Having ImprovedMating Accuracy.” The '809 application is also a CIP application of the'344 application. The '388 application is also a CIP of the '344application. The '388 application is also a CIP of the '924 application.And the '388 application is also a CIP of the '056 application.

The present application claims priority to all of the above mentionedapplications and hereby incorporates by reference all of theabove-mentioned applications in their entireties into the presentapplication.

FIELD OF THE INVENTION

The present invention relates to image segmentation, morphing bonemodels to pre-degenerated states, and planning surgeries.

BACKGROUND OF THE INVENTION

Over time and through repeated use, bones and joints can become damagedor worn. For example, repetitive strain on bones and joints (e.g.,through athletic activity), traumatic events, and certain diseases(e.g., arthritis) can cause cartilage in joint areas, which normallyprovides a cushioning effect, to wear down. Cartilage wearing down canresult in fluid accumulating in the joint areas, pain, stiffness, anddecreased mobility.

Arthroplasty procedures can be used to repair damaged joints. During atypical arthroplasty procedure, an arthritic or otherwise dysfunctionaljoint can be remodeled or realigned, or an implant can be implanted intothe damaged region. Arthroplasty procedures may take place in any of anumber of different regions of the body, such as a knee, a hip, ashoulder, or an elbow.

One type of arthroplasty procedure is a total knee arthroplasty (“TKA”),in which a damaged knee joint is replaced with prosthetic implants. Theknee joint may have been damaged by, for example, arthritis (e.g.,severe osteoarthritis or degenerative arthritis), trauma, or a raredestructive joint disease. During a TKA procedure, a damaged portion inthe distal region of the femur may be removed and replaced with a metalshell, and a damaged portion in the proximal region of the tibia may beremoved and replaced with a channeled piece of plastic having a metalstem. In some TKA procedures, a plastic button may also be added underthe surface of the patella, depending on the condition of the patella.

Implants that are implanted into a damaged region may provide supportand structure to the damaged region, and may help to restore the damagedregion, thereby enhancing its functionality. Prior to implantation of animplant in a damaged region, the damaged region may be prepared toreceive the implant. For example, in a knee arthroplasty procedure, oneor more of the bones in the knee area, such as the femur and/or thetibia, may be treated (e.g., cut, drilled, reamed, and/or resurfaced) toprovide one or more surfaces that can align with the implant and therebyaccommodate the implant.

Accuracy in implant alignment is an important factor to the success of aTKA procedure. A one- to two-millimeter translational misalignment, or aone- to two-degree rotational misalignment, may result in imbalancedligaments, and may thereby significantly affect the outcome of the TKAprocedure. For example, implant misalignment may result in intolerablepost-surgery pain, and also may prevent the patient from having full legextension and stable leg flexion.

To achieve accurate implant alignment, prior to treating (e.g., cutting,drilling, reaming, and/or resurfacing) any regions of a bone, it isimportant to correctly determine the location at which the treatmentwill take place and how the treatment will be oriented. In some methods,an arthroplasty jig may be used to accurately position and orient afinishing instrument, such as a cutting, drilling, reaming, orresurfacing instrument on the regions of the bone. The arthroplasty jigmay, for example, include one or more apertures and/or slots that areconfigured to accept such an instrument.

A system and method has been developed for producing customizedarthroplasty jigs configured to allow a surgeon to accurately andquickly perform an arthroplasty procedure that restores thepre-deterioration alignment of the joint, thereby improving the successrate of such procedures. Specifically, the customized arthroplasty jigsare indexed such that they matingly receive the regions of the bone tobe subjected to a treatment (e.g., cutting, drilling, reaming, and/orresurfacing). The customized arthroplasty jigs are also indexed toprovide the proper location and orientation of the treatment relative tothe regions of the bone. The indexing aspect of the customizedarthroplasty jigs allows the treatment of the bone regions to be donequickly and with a high degree of accuracy that will allow the implantsto restore the patient's joint to a generally pre-deteriorated state.However, the system and method for generating the customized jigs oftenrelies on a human to “eyeball” bone models on a computer screen todetermine configurations needed for the generation of the customizedjigs. This “eyeballing” or manual manipulation of the bone modes on thecomputer screen is inefficient and unnecessarily raises the time,manpower and costs associated with producing the customized arthroplastyjigs. Furthermore, a less manual approach may improve the accuracy ofthe resulting jigs.

There is a need in the art for a system and method for reducing thelabor associated with generating customized arthroplasty jigs. There isalso a need in the art for a system and method for increasing theaccuracy of customized arthroplasty jigs.

SUMMARY

Systems and methods for image segmentation in generating computer modelsof a joint to undergo arthroplasty are disclosed. Some embodiments mayinclude a method of partitioning an image of a bone into a plurality ofregions, where the method of partitioning may include obtaining aplurality of volumetric image slices of the bone, generating a pluralityof spline curves associated with the bone, verifying that at least oneof the plurality of spline curves follow a surface of the bone, andcreating a three dimensional (3D) mesh representation based upon the atleast one of the plurality of spline curves.

Aspects of the present disclosure may involve a computer-implementedmethod of preoperatively planning a surgical procedure on a knee of apatient, where the knee joins together a femur having femoral condylesand a tibia having a tibial plateau. The surgical procedure may includeimplanting an implant on at least one of the femur and the tibia as partof the procedure. The method may include determining femoral condylevectors and tibial plateau vectors based on image data of the knee. Thefemoral condyle vectors and the tibial plateau vectors may correspond tomotion vectors of the femoral condyles and the tibial plateau as theymove relative to each other. The method may further include modifying abone model representative of at least one of the femur and the tibiainto a modified bone model based on the femoral condyle vectors and thetibial plateau vectors. And the method may further include determiningcoordinate locations for a resection of the modified bone model.

In certain instances, modifying the bone model may include modifying ashape of femoral condyles of the bone model. In certain instances,modifying the bone model may include modifying a shape of a tibialplateau of the bone model. In certain instances, modifying the bonemodel may include restoring a surface of the bone model to a lessdegenerated state.

In certain instances, the bone model is a femoral bone model and atibial bone model.

In certain instances, the modified bone model may include a modificationto a surface profile of the bone model.

In certain instances, the modified bone model is a restored bone modelwith the surface profile being modified from a degenerated state to aless degenerated state.

In certain instances, the image data of the knee may includepreoperatively generated medical images.

In certain instances, the image data of the knee may include twodimensional image views of the knee, and the femoral condyle vectors andtibial plateau vectors are determined based on an analysis of geometricfeatures of the femoral condyles and tibial plateau in the twodimensional image views of the knee.

In certain instances, determining coordinate locations for a resectionof the modified bone model may include: aligning points on an implantmodel with corresponding points on the modified bone model, the implantmodel is positioned and oriented relative to the modified bone model ina coordinate system that is reflective of the implant being implanted onthe femur.

In certain instances, the points on the modified bone model may includea first point at a most distal location on a condylar surface of themodified bone model and a second point on a location on the condylarsurface of the modified bone model that is proximal to the first point.

In certain instances, the points on the implant model may include athird point at a most distal location on a condylar surface of theimplant model and a fourth point on a location on the condylar surfaceof the implant model that is proximal to the third point.

In certain instances, determining coordinate locations for a resectionof the modified bone model may include: aligning a point on an implantmodel with a corresponding point on the modified bone model, the implantmodel is positioned and oriented relative to the modified bone model ina coordinate system that is reflective of the implant being implanted onthe tibia.

In certain instances, the point on the modified bone model may include afirst point at a most distally recessed location on a condylar surfaceof the modified bone model.

In certain instances, the point on the implant model may include asecond point at a most distally recessed location on a condylar surfaceof the implant model.

In certain instances, determining coordinate locations for a resectionof the modified bone model may include automatically identifying apreliminary position and orientation of the resection.

Aspects of the present disclosure may involve a method of planning andperforming a surgical procedure on a knee of a patient where the kneejoins together a femur having femoral condyles and a tibia having atibial plateau. The method may include performing a motion analysis ofthe knee, whereby a 3D bone model representing at least one of the femurand tibia is modified into a modified 3D bone model based on the motionanalysis of the knee. And the method may include determining coordinatelocations for a resection of the modified bone model.

In certain instances, performing the motion analysis of the knee mayinclude using a computer to determine femoral condyle vectors and tibialplateau vectors corresponding to motion vectors of the femoral condylesand the tibial plateau as they move relative to each other.

In certain instances, the bone model is modified into the modified bonemodel based on femoral condyle vectors and tibial plateau vectors.

In certain instances, further may include performing the surgicalprocedure including cutting a physical resection on the patient at theknee at a location that corresponds to the resection of the modifiedbone model.

While multiple embodiments are disclosed, still other embodiments of thepresent invention will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments of the invention. As will be realized, theinvention is capable of modifications in various aspects, all withoutdeparting from the spirit and scope of the present invention.Accordingly, the drawings and detailed description are to be regarded asillustrative in nature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic diagram of a system for employing the automatedjig production method disclosed herein.

FIGS. 1B-1E are flow chart diagrams outlining the jig production methoddisclosed herein.

FIGS. 1F and 1G are, respectively, bottom and top perspective views ofan example customized arthroplasty femur jig.

FIGS. 1H and 11 are, respectively, bottom and top perspective views ofan example customized arthroplasty tibia jig.

FIG. 2A is a sagittal plane image slice depicting a femur and tibia andneighboring tissue regions with similar image intensity.

FIG. 2B is a sagittal plane image slice depicting a region extendinginto the slice from an adjacent image slice.

FIG. 2C is a sagittal plane image slice depicting a region of a femurthat is approximately tangent to the image slice.

FIG. 3A is a sagittal plane image slice depicting an intensity gradientacross the slice.

FIG. 3B is a sagittal plane image slice depicting another intensitygradient across the slice.

FIG. 3C is a sagittal plane image slice depicting another intensitygradient across the slice.

FIG. 4A depicts a sagittal plane image slice with a high noise level.

FIG. 4B depicts a sagittal plane image slice with a low noise level.

FIG. 5 is a sagittal plane image slice of a femur and tibia depictingregions where good definition may be needed during automaticsegmentation of the femur and tibia.

FIG. 6 depicts a flowchart illustrating one method for automaticsegmentation of an image modality scan of a patient's knee joint.

FIG. 7A is a sagittal plane image slice of a segmented femur.

FIG. 7B is a sagittal plane image slice of a segmented femur and tibia.

FIG. 7C is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7D is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7E is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7F is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7G is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7H is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7I is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7J is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7K is another sagittal plane image slice of a segmented femur andtibia.

FIG. 8 is a sagittal plane image slice depicting automatically generatedslice curves of a femur and a tibia.

FIG. 9 depicts a 3D mesh geometry of a femur.

FIG. 10 depicts a 3D mesh geometry of a tibia.

FIG. 11 depicts a flowchart illustrating one method for generating agolden template.

FIG. 12A is a sagittal plane image slice depicting a contour curveoutlining a golden tibia region, a contour curve outlining a grown tibiaregion and a contour curve outlining a boundary golden tibia region.

FIG. 12B is a sagittal plane image slice depicting a contour curveoutlining a golden femur region, a contour curve outlining a grown femurregion and a contour curve outlining a boundary golden femur region.

FIG. 13A depicts a golden tibia 3D mesh.

FIG. 13B depicts a golden femur 3D mesh.

FIG. 14A is a sagittal plane image slice depicting anchor segmentationregions of a tibia.

FIG. 14B is a sagittal plane image slice depicting anchor segmentationregions of a femur.

FIG. 15A is a 3D mesh geometry depicting the anchor segmentation mesh,the InDark-OutLight anchor mesh, the InLight-OutDark anchor mesh, andthe Dark-In-Light anchor mesh of a tibia.

FIG. 15B is a 3D mesh geometry depicting the anchor segmentation mesh,the InDark-OutLight anchor mesh and the InLight-OutDark anchor mesh of afemur.

FIG. 16 depicts a flowchart illustrating one method for performingautomatic segmentation of scan data using golden template registration.

FIG. 17 depicts a flowchart illustrating one method for mapping thesegmented golden femur template regions into the target scan data usingimage registration techniques.

FIG. 18 depicts a registration framework that may be employed by oneembodiment.

FIG. 19 depicts a flowchart illustrating one method for mapping thesegmented golden tibia template regions into the target scan data usingimage registration techniques.

FIG. 20 depicts a flowchart illustrating one method for computing ametric for the registration framework of FIG. 18.

FIG. 21 depicts a flowchart illustrating one method for refining theregistration results using anchor segmentation and anchor regions.

FIG. 22 depicts a set of randomly generated light sample points and darksample points of a tibia.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves to outline features of interest in each target MRI slice.

FIG. 24 depicts a polyline curve with n vertices.

FIG. 25 depicts a flowchart illustrating one method for adjustingsegments.

FIG. 26 is a sagittal plane image slice depicting a contour curve withcontrol points outlining a femur with superimposed contour curves of thefemur from adjacent image slices.

FIG. 27 depicts a 3D slice visualization of a femur showing the voxelsinside of the spline curves.

FIG. 28 is a diagram depicting types of data employed in the imagesegmentation algorithm that uses landmarks.

FIG. 29 is a flowchart illustrating the overall process for generating agolden femur model of FIG. 28.

FIG. 30 is an image slice of the representative femur to be used togenerate a golden femur mesh.

FIG. 31A is an isometric view of a closed golden femur mesh.

FIG. 31B is an isometric view of an open golden femur mesh created fromthe closed golden femur mesh of FIG. 31A.

FIG. 31C is the open femur mesh of FIG. 31B with regions of a differentprecision indicated.

FIGS. 32A-32B are isometric views of an open golden tibia mesh withregions of a different precision indicated.

FIG. 33 is a flow chart illustrating an alternative method of segmentingan image slice, the alternative method employing landmarks.

FIG. 34 is a flow chart illustrating the process involved in operation“position landmarks” of the flow chart of FIG. 33.

FIGS. 35A-35H are a series of sagittal image slices wherein landmarkshave been placed according the process of FIG. 34.

FIG. 36 is a flowchart illustrating the process of segmenting the targetimages that were provided with landmarks in operation “positionlandmarks” of the flow chart of FIG. 33.

FIG. 37 is a flowchart illustrating the process of operation “DeformGolden Femur Mesh” of FIG. 36, the process including mapping the goldenfemur mesh into the target scan using registration techniques.

FIG. 38A is a flowchart illustrating the process of operation “DetectAppropriate Image Edges” of FIG. 37.

FIG. 38B is an image slice with a contour line representing theapproximate segmentation mesh surface found in operation 770 c of FIG.37, the vectors showing the gradient of the signed distance for thecontour.

FIG. 38C is an enlarged view of the area in FIG. 38B enclosed by thesquare 1002, the vectors showing the computed gradient of the targetimage.

FIG. 38D is the same view as FIG. 38C, except the vectors of FIGS. 38Band 38C are superimposed.

FIG. 39 is a flowchart illustrating the process of operation “ModifySplines” of FIG. 36.

FIG. 40 is an image slice with a spline being modified according to theoperations of the flow chart of FIG. 39.

FIG. 41 is a diagram generally illustrating a bone restoration processfor restoring a 3D computer generated bone model into a 3D computergenerated restored bone model.

FIG. 42A is a coronal view of a distal or knee joint end of a femurrestored bone model.

FIG. 42B is an axial view of a distal or knee joint end of a femurrestored bone model.

FIG. 42C is a coronal view of a proximal or knee joint end of a tibiarestored bone model.

FIG. 42D represents the femur and tibia restored bone models in theviews depicted in FIGS. 42A and 42C positioned together to form a kneejoint.

FIG. 42E represents the femur and tibia restored bone models in theviews depicted in FIGS. 42B and 42C positioned together to form a kneejoint.

FIG. 42F is a sagittal view of the femoral medial condyle ellipse and,more specifically, the N1 slice of the femoral medial condyle ellipse astaken along line N1 in FIG. 42A.

FIG. 42G is a sagittal view of the femoral lateral condyle ellipse and,more specifically, the N2 slice of the femoral lateral condyle ellipseas taken along line N2 in FIG. 42A.

FIG. 42H is a sagittal view of the femoral medial condyle ellipse and,more specifically, the N3 slice of the femoral medial condyle ellipse astaken along line N3 in FIG. 42B.

FIG. 42I is a sagittal view of the femoral lateral condyle ellipse and,more specifically, the N4 slice of the femoral lateral condyle ellipseas taken along line N4 in FIG. 42B.

FIG. 43A is a sagittal view of the lateral tibia plateau with thelateral femur condyle ellipse of the N1 slice of FIG. 42F superimposedthereon.

FIG. 43B is a sagittal view of the medial tibia plateau with the lateralfemur condyle ellipse of the N2 slice of FIG. 42G superimposed thereon.

FIG. 43C is a top view of the tibia plateaus of a restored tibia bonemodel.

FIG. 43D is a sagittal cross section through a lateral tibia plateau ofthe restored bone model 28B of FIG. 43C and corresponding to the N3image slice of FIG. 42B.

FIG. 43E is a sagittal cross section through a medial tibia plateau ofthe restored bone model of FIG. 43C and corresponding to the N4 imageslice of FIG. 42B.

FIG. 43F is a posterior-lateral perspective view of femur and tibia bonemodels forming a knee joint.

FIG. 43G is a posterior-lateral perspective view of femur and tibiarestored bone models forming a knee joint.

FIG. 44A is a coronal view of a femur bone model.

FIG. 44B is a coronal view of a tibia bone model.

FIG. 44C1 is an N2 image slice of the medial condyle as taken along theN2 line in FIG. 44A.

FIG. 44C2 is the same view as FIG. 44C1, except illustrating the need toincrease the size of the reference information prior to restoring thecontour line of the N2 image slice.

FIG. 44C3 is the same view as FIG. 44C1, except illustrating the need toreduce the size of the reference information prior to restoring thecontour line of the N2 image slice.

FIG. 44D is the N2 image slice of FIG. 44C1 subsequent to restoration.

FIG. 44E is a sagittal view of the medial tibia plateau along the N4image slice, wherein damage to the plateau is mainly in the posteriorregion.

FIG. 44F is a sagittal view of the medial tibia plateau along the N4image slice, wherein damage to the plateau is mainly in the anteriorregion.

FIG. 44G is the same view as FIG. 44E, except showing the reference sidefemur condyle vector extending through the anterior highest point of thetibia plateau.

FIG. 44H is the same view as FIG. 44F, except showing the reference sidefemur condyle vector extending through the posterior highest point ofthe tibia plateau.

FIG. 44I is the same view as FIG. 44G, except showing the anteriorhighest point of the tibia plateau restored.

FIG. 44J is the same view as FIG. 44H, except showing the posteriorhighest point of the tibia plateau restored.

FIG. 44K is the same view as FIG. 44G, except employing reference vectorV₁ as opposed to U₁.

FIG. 44L is the same view as FIG. 44H, except employing reference vectorV₁ as opposed to U₁.

FIG. 44M is the same view as FIG. 44I, except employing reference vectorV₁ as opposed to U₁.

FIG. 44N is the same view as FIG. 44J, except employing reference vectorV₁ as opposed to U₁.

FIG. 45A is a sagittal view of a femur restored bone model illustratingthe orders and orientations of imaging slices (e.g., MRI slices, CTslices, etc.) forming the femur restored bone model.

FIG. 45B is the distal images slices 1-5 taken along section lines 1-5of the femur restored bone model in FIG. 45A.

FIG. 45C is the coronal images slices 6-8 taken along section lines 6-8of the femur restored bone model in FIG. 45A.

FIG. 45D is a perspective view of the distal end of the femur restoredbone model.

FIG. 46 is a table illustrating how OA knee conditions may impact thelikelihood of successful bone restoration.

FIGS. 47A-47D are various views of the tibia plateau with reference torestoration of a side thereof.

FIGS. 48A and 48B are, respectively, coronal and sagittal views of therestored bone models.

FIG. 49A is a diagram illustrating the condition of a patient's rightknee, which is in a deteriorated state, and left knee, which isgenerally healthy.

FIG. 49B is a diagram illustrating two options for creating a restoredbone model for a deteriorated right knee from image slices obtained froma healthy left knee.

FIGS. 50A-50E are flow chart diagrams outlining the jig productionmethod disclosed herein.

FIGS. 51A and 51B are, respectively, bottom and top perspective views ofan example customized arthroplasty femur jig.

FIGS. 51C and 51D are, respectively, top/posterior and bottom/anteriorperspective views of an example customized arthroplasty tibia jig.

FIG. 52A is an isometric view of a 3D computer model of a femur lowerend and a 3D computer model of a tibia upper end in position relative toeach to form a knee joint and representative of the femur and tibia in anon-degenerated state.

FIG. 52B is an isometric view of a 3D computer model of a femur implantand a 3D computer model of a tibia implant in position relative to eachto form an artificial knee joint.

FIG. 53 is a perspective view of the distal end of 3D model of the femurwherein the femur reference data is shown.

FIG. 54A is a sagittal view of a femur illustrating the orders andorientations of imaging slices utilized in the femur POP.

FIG. 54B depicts axial imaging slices taken along section lines of thefemur of FIG. 54A.

FIG. 54C depicts coronal imaging slices taken along section lines of thefemur of FIG. 54A.

FIG. 55A is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the distal reference points are shown.

FIG. 55B is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the trochlear groove bisector line is shown.

FIG. 55C is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the femur reference data is shown.

FIG. 55D is the axial imaging slices taken along section lines of thefemur in FIG. 54A.

FIG. 56A is a coronal slice taken along section lines of the femur ofFIG. 54A, wherein the femur reference data is shown

FIG. 56B is the coronal imaging slices taken along section lines of thefemur in FIG. 54A.

FIG. 56C is a sagittal imaging slice of the femur in FIG. 54A.

FIG. 56D is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the femur reference data is shown.

FIG. 56E is a coronal imaging slice taken along section lines of thefemur of FIG. 54A, wherein the femur reference data is shown.

FIG. 57 is a posterior view of a 3D model of a distal femur.

FIG. 58 depicts a y-z coordinate system wherein the femur reference datais shown.

FIG. 59 is a perspective view of a femoral implant model, wherein thefemur implant reference data is shown.

FIG. 60 is another perspective view of a femoral implant model, whereinthe femur implant reference data is shown.

FIG. 61 is a y-z coordinate system wherein some of the femur implantreference data is shown.

FIG. 62 is an x-y-z coordinate system wherein the femur implantreference data is shown.

FIG. 63A shows the femoral condyle and the proximal tibia of the knee ina sagittal view MRI image slice.

FIG. 63B is a coronal view of a knee model in extension.

FIGS. 63C and 63D illustrate MRI segmentation slices for joint lineassessment.

FIG. 63E is a flow chart illustrating the method for determiningcartilage thickness used to determine proper joint line.

FIG. 63F illustrates a MRI segmentation slice for joint line assessment.

FIGS. 63G and 63H illustrate coronal views of the bone images in theiralignment relative to each as a result of OA.

FIG. 63I illustrates a coronal view of the bone images with a restoredgap Gp3.

FIG. 63J is a coronal view of bone images oriented relative to eachother in a deteriorated state orientation.

FIG. 64 is a 3D coordinate system wherein the femur reference data isshown.

FIG. 65 is a y-z plane wherein the joint compensation points are shown.

FIG. 66 illustrates the implant model 34′ placed onto the samecoordinate plane with the femur reference data.

FIG. 67A is a plan view of the joint side of the femur implant modeldepicted in FIG. 52B.

FIG. 67B is an axial end view of the femur lower end of the femur bonemodel depicted in FIG. 52A.

FIG. 67C illustrates the implant extents iAP and iML and the femurextents bAP, bML as they may be aligned for proper implant placement.

FIG. 68A shows the most medial edge of the femur in a 2D sagittalimaging slice.

FIG. 68B, illustrates the most lateral edge of the femur in a 2Dsagittal imaging slice.

FIG. 68C is a 2D imaging slice in coronal view showing the medial andlateral edges.

FIG. 69A is a candidate implant model mapped onto a y-z plane.

FIG. 69B is the silhouette curve of the articular surface of thecandidate implant model.

FIG. 69C is the silhouette curve of the candidate implant model alignedwith the joint spacing compensation points D_(1J)D_(2J) andP_(1J)P_(2J).

FIG. 70A illustrates a sagittal imaging slice of a distal femur with animplant model.

FIG. 70B depicts a femur implant model wherein the flange point on theimplant is shown.

FIG. 70C shows an imaging slice of the distal femur in the sagittalview, wherein the inflection point located on the anterior shaft of thespline is shown.

FIG. 70D illustrates the 2D implant model properly positioned on the 2Dfemur image, as depicted in a sagittal view.

FIG. 71A depicts an implant model that is improperly aligned on a 2Dfemur image, as depicted in a sagittal view.

FIG. 71B illustrates the implant positioned on a femur transform whereina femur cut plane is shown, as depicted in a sagittal view.

FIG. 72 is a top view of the tibia plateaus of a tibia bone image ormodel.

FIG. 73A is a sagittal cross section through a lateral tibia plateau ofthe 2D tibia bone model or image.

FIG. 73B is a sagittal cross section through a medial tibia plateau ofthe 2D tibia bone model or image.

FIG. 73C depicts a sagittal cross section through an undamaged or littledamaged medial tibia plateau of the 2D tibia model, wherein osteophytesare also shown.

FIG. 73D is a sagittal cross section through a damaged lateral tibiaplateau of the 2D tibia model.

FIG. 74A is a coronal 2D imaging slice of the tibia.

FIG. 74B is an axial 2D imaging slice of the tibia.

FIG. 75A depicts the tibia reference data on an x-y coordinate system.

FIG. 75B depicts the tibia reference data on a proximal end of the tibiato aid in the selection of an appropriate tibia implant.

FIG. 76A is a 2D sagittal imaging slice of the tibia wherein asegmentation spline with an AP extant is shown.

FIG. 76B is an axial end view of the tibia upper end of the tibia bonemodel depicted in FIG. 52A.

FIG. 76C is a plan view of the joint side of the tibia implant modeldepicted in FIG. 52B.

FIG. 77 is a top isometric view of the tibia plateaus of a tibia implantmodel.

FIG. 78A is an isometric view of the 3D tibia bone model showing thesurgical cut plane SCP design.

FIGS. 78B and 78C are sagittal MRI views of the surgical tibia cut planeSCP design with the posterior cruciate ligament PCL.

FIG. 79A is an isometric view of the tibia implant wherein a cut planeis shown.

FIG. 79B is a top axial view of the implant superimposed on the tibiareference data.

FIG. 79C is an axial view of the tibial implant aligned with the tibiareference data.

FIG. 79D is a MRI imaging slice of the medial portion of the proximaltibia and indicates the establishment of landmarks for the tibia POPdesign, as depicted in a sagittal view.

FIG. 79E is a MRI imaging slice of the lateral portion of the proximaltibia, as depicted in a sagittal view.

FIG. 79F is an isometric view of the 3D model of the tibia implant andthe cut plane.

FIGS. 80A-80B are sagittal views of a 2D imaging slice of the femurwherein the 2D computer generated implant models are also shown.

FIG. 80C is a sagittal view of a 2D imaging slice of the tibia whereinthe 2D computer generated implant model is also shown.

FIGS. 81A-81C are various views of the 2D implant models superimposed onthe 2D bone models.

FIG. 81D is a coronal view of the 2D bone models.

FIGS. 81E-81G are various views of the 2D implant models superimposed onthe 2D bone models.

FIG. 82A is a medial view of the 3D bone models.

FIG. 82B is a medial view of the 3D implant models

FIG. 82C is a medial view of the 3D implant models superimposed on the3D bone models.

DETAILED DESCRIPTION

Disclosed herein are customized arthroplasty jigs 2 and systems 4 for,and methods of, producing such jigs 2. The jigs 2 are customized to fitspecific bone surfaces of specific patients. Depending on the embodimentand to a greater or lesser extent, the jigs 2 are automatically plannedand generated and may be similar to those disclosed in these three U.S.patent applications: U.S. patent application Ser. No. 11/656,323 to Parket al., titled “Arthroplasty Devices and Related Methods” and filed Jan.19, 2007; U.S. patent application Ser. No. 10/146,862 to Park et al.,titled “Improved Total Joint Arthroplasty System” and filed May 15,2002; and U.S. patent Ser. No. 11/642,385 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006. Thedisclosures of these three U.S. patent applications are incorporated byreference in their entireties into this Detailed Description.

As an overview, Section I. of the present disclosure provides adescription of systems and methods of manufacturing custom arthroplastycutting guides. Section II. of the present disclosure provides anoverview of exemplary segmentation processes performed on medicalimages, and the generation of bone models representing bones of a jointin a deteriorated state. Section III. of the present disclosuredescribes an overview of a bone restoration process where image data ofa patient's bones in a deteriorated state is restored to apre-deteriorated state, and where the restored image data may be used togenerate a restored bone model representing the patient bone in apre-deteriorated or pre-degenerated state. And Section IV. of thepresent disclosure provides an overview of the pre-operative surgicalplanning process that may take place on the patient's image data (e.g.,image data of deteriorated bone or restored image data).

I. Overview of System and Method for Manufacturing CustomizedArthroplasty Cutting Jigs

For an overview discussion of the systems 4 for, and methods of,producing the customized arthroplasty jigs 2, reference is made to FIGS.1A-1E. FIG. 1A is a schematic diagram of a system 4 for employing theautomated jig production method disclosed herein. FIGS. 1B-1E are flowchart diagrams outlining the jig production method disclosed herein. Thefollowing overview discussion can be broken down into three sections.

The first section, which is discussed with respect to FIG. 1A and[blocks 100-125] of FIGS. 1B-1E, pertains to an example method ofdetermining, in a three-dimensional (“3D”) computer model environment,saw cut and drill hole locations 30, 32 relative to 3D computer modelsthat are termed restored bone models 28 (also referenced as “planningmodels” throughout this submission.) In some embodiments, the resulting“saw cut and drill hole data” 44 is referenced to the restored bonemodels 28 to provide saw cuts and drill holes that will allowarthroplasty implants to restore the patient's joint to itspre-degenerated state. In other words, in some embodiments, thepatient's joint may be restored to its natural alignment, whethervalgus, varus or neutral.

While many of the embodiments disclosed herein are discussed withrespect to allowing the arthroplasty implants to restore the patient'sjoint to its pre-degenerated or natural alignment state, many of theconcepts disclosed herein may be applied to embodiments wherein thearthroplasty implants restore the patient's joint to a zero mechanicalaxis alignment such that the patient's knee joint ends up being neutral,regardless of whether the patient's predegenerated condition was varus,valgus or neutral. For example, as disclosed in U.S. patent applicationSer. No. 12/760,388 to Park et al., titled “Preoperatively Planning AnArthroplasty Procedure And Generating A Corresponding Patient SpecificArthroplasty Resection Guide”, filed Apr. 14, 2010, and incorporated byreference into this Detailed Description in its entirety, the system 4for producing the customized arthroplasty jigs 2 may be such that thesystem initially generates the preoperative planning (“POP”) associatedwith the jig in the context of the POP resulting in the patient's kneebeing restored to its natural alignment. Such a natural alignment POP isprovided to the physician, and the physician determines if the POPshould result in (1) natural alignment, (2) mechanical alignment, or (3)something between (1) and (2). The POP is then adjusted according to thephysician's determination, the resulting jig 2 being configured suchthat the arthroplasty implants will restore the patient's joint to (1),(2) or (3), depending on whether the physician elected (1), (2) or (3),respectively. Accordingly, this disclosure should not be limited tomethods resulting in natural alignment only, but should, whereappropriate, be considered as applicable to methods resulting in naturalalignment, zero mechanical axis alignment or an alignment somewherebetween natural and zero mechanical axis alignment.

The second section, which is discussed with respect to FIG. 1A and[blocks 100-105 and 130-145] of FIGS. 1B-1E, pertains to an examplemethod of importing into 3D computer generated jig models 38 3D computergenerated surface models 40 of arthroplasty target areas 42 of 3Dcomputer generated arthritic models 36 of the patient's joint bones. Theresulting “jig data” 46 is used to produce a jig customized to matinglyreceive the arthroplasty target areas of the respective bones of thepatient's joint.

The third section, which is discussed with respect to FIG. 1A and[blocks 150-165] of FIG. 1E, pertains to a method of combining orintegrating the “saw cut and drill hole data” 44 with the “jig data” 46to result in “integrated jig data” 48. The “integrated jig data” 48 isprovided to the CNC machine 10 for the production of customizedarthroplasty jigs 2 from jig blanks 50 provided to the CNC machine 10.The resulting customized arthroplasty jigs 2 include saw cut slots anddrill holes positioned in the jigs 2 such that when the jigs 2 matinglyreceive the arthroplasty target areas of the patient's bones, the cutslots and drill holes facilitate preparing the arthroplasty target areasin a manner that allows the arthroplasty joint implants to generallyrestore the patient's joint line to its pre-degenerated state.

As shown in FIG. 1A, the system 4 includes one or more computers 6having a CPU 7, a monitor or screen 9 and an operator interface controls11. The computer 6 is linked to a medical imaging system 8, such as a CTor MRI machine 8, and a computer controlled machining system 10, such asa CNC milling machine 10.

In another embodiment, rather than using a single computer for the wholeprocess, multiple computers can perform separate steps of the overallprocess, with each respective step managed by a respective technicianskilled in that particular aspect of the overall process. The datagenerated in one process step on one computer can be then transferredfor the next process step to another computer, for instance via anetwork connection.

As indicated in FIG. 1A, a patient 12 has a joint 14 (e.g., a knee,elbow, ankle, wrist, hip, shoulder, skull/vertebrae orvertebrae/vertebrae interface, etc.) to be replaced. The patient 12 hasthe joint 14 scanned in the imaging machine 8. The imaging machine 8makes a plurality of scans of the joint 14, wherein each scan pertainsto a thin slice of the joint 14.

As can be understood from FIG. 1B, the plurality of scans is used togenerate a plurality of two-dimensional (“2D”) images 16 of the joint 14[block 100]. Where, for example, the joint 14 is a knee 14, the 2Dimages will be of the femur 18 and tibia 20. The imaging may beperformed via CT or MRI. In one embodiment employing MRI, the imagingprocess may be as disclosed in U.S. patent application Ser. No.11/946,002 to Park, which is entitled “Generating MRI Images Usable ForThe Creation Of 3D Bone Models Employed To Make Customized ArthroplastyJigs,” was filed Nov. 27, 2007 and is incorporated by reference in itsentirety into this Detailed Description.

As can be understood from FIG. 1A, the 2D images are sent to thecomputer 6 for creating computer generated 3D models. As indicated inFIG. 1B, in one embodiment, point P is identified in the 2D images 16[block 105]. In one embodiment, as indicated in [block 105] of FIG. 1A,point P may be at the approximate medial-lateral and anterior-posteriorcenter of the patient's joint 14. In other embodiments, point P may beat any other location in the 2D images 16, including anywhere on, nearor away from the bones 18, 20 or the joint 14 formed by the bones 18,20.

As described later in this overview, point P may be used to locate thecomputer generated 3D models 22, 28, 36 created from the 2D images 16and to integrate information generated via the 3D models. Depending onthe embodiment, point P, which serves as a position and/or orientationreference, may be a single point, two points, three points, a point plusa plane, a vector, etc., so long as the reference P can be used toposition and/or orient the 3D models 22, 28, 36 generated via the 2Dimages 16.

As discussed in detail below, the 2D images 16 are segmented along boneboundaries to create bone contour lines. Also, the 2D images 16 aresegmented along bone and cartilage boundaries to create bone andcartilage lines.

As shown in FIG. 1C, the segmented 2D images 16 (i.e., bone contourlines) are employed to create computer generated 3D bone-only (i.e.,“bone models”) 22 of the bones 18, 20 forming the patient's joint 14[block 110]. The bone models 22 are located such that point P is atcoordinates (X_(P), Y_(P), Z_(P)) relative to an origin (X₀, Y₀, Z₀) ofan X-Y-Z coordinate system [block 110]. The bone models 22 depict thebones 18, 20 in the present deteriorated condition with their respectivedegenerated joint surfaces 24, 26, which may be a result ofosteoarthritis, injury, a combination thereof, etc.

Computer programs for creating the 3D computer generated bone models 22from the segmented 2D images 16 (i.e., bone contour lines) include:Analyze from AnalyzeDirect, Inc., Overland Park, Kans.; Insight Toolkit,an open-source software available from the National Library of MedicineInsight Segmentation and Registration Toolkit (“ITK”), www.itk.org; 3DSlicer, an open-source software available from www.slicer.org; Mimicsfrom Materialise, Ann Arbor, Mich.; and Paraview available atwww.paraview.org. Further, some embodiments may use customized softwaresuch as OMSegmentation (renamed “PerForm” in later versions), developedby OtisMed, Inc. The OMSegmentation software may extensively use “ITK”and/or “VTK” (Visualization Toolkit from Kitware, Inc, available atwww.vtk.org.) Some embodiments may include using a prototype ofOMSegmentation, and as such may utilize InsightSNAP software.

As indicated in FIG. 1C, the 3D computer generated bone models 22 areutilized to create 3D computer generated “restored bone models” or“planning bone models” 28 wherein the degenerated surfaces 24, 26 aremodified or restored to approximately their respective conditions priorto degeneration [block 115]. Thus, the bones 18, 20 of the restored bonemodels 28 are reflected in approximately their condition prior todegeneration. The restored bone models 28 are located such that point Pis at coordinates (X_(P), Y_(P), Z_(P)) relative to the origin (X₀, Y₀,Z₀). Thus, the restored bone models 28 share the same orientation andpositioning relative to the origin (X₀, Y₀, Z₀) as the bone models 22.

In one embodiment, the restored bone models 28 are manually created fromthe bone models 22 by a person sitting in front of a computer 6 andvisually observing the bone models 22 and their degenerated surfaces 24,26 as 3D computer models on a computer screen 9. The person visuallyobserves the degenerated surfaces 24, 26 to determine how and to whatextent the degenerated surfaces 24, 26 surfaces on the 3D computer bonemodels 22 need to be modified to restore them to their pre-degeneratedcondition. By interacting with the computer controls 11, the person thenmanually manipulates the 3D degenerated surfaces 24, 26 via the 3Dmodeling computer program to restore the surfaces 24, 26 to a state theperson believes to represent the pre-degenerated condition. The resultof this manual restoration process is the computer generated 3D restoredbone models 28, wherein the surfaces 24′, 26′ are indicated in anon-degenerated state.

In one embodiment, the bone restoration process is generally orcompletely automated. In other words, a computer program may analyze thebone models 22 and their degenerated surfaces 24, 26 to determine howand to what extent the degenerated surfaces 24, 26 surfaces on the 3Dcomputer bone models 22 need to be modified to restore them to theirpre-degenerated condition. The computer program then manipulates the 3Ddegenerated surfaces 24, 26 to restore the surfaces 24, 26 to a stateintended to represent the pre-degenerated condition. The result of thisautomated restoration process is the computer generated 3D restored bonemodels 28, wherein the surfaces 24′, 26′ are indicated in anon-degenerated state. For more detail regarding a generally orcompletely automated system for the bone restoration process, see U.S.patent application Ser. No. 12/111,924 to Park, which is titled“Generation of a Computerized Bone Model Representative of aPre-Degenerated State and Usable in the Design and Manufacture ofArthroplasty Devices”, was filed Apr. 29, 2008, and is incorporated byreference in its entirety into this Detailed Description.

As depicted in FIG. 1C, the restored bone models 28 are employed in apre-operative planning (“POP”) procedure to determine saw cut locations30 and drill hole locations 32 in the patient's bones that will allowthe arthroplasty joint implants to generally restore the patient's jointline to it pre-degenerative alignment [block 120].

In one embodiment, the POP procedure is a manual process, whereincomputer generated 3D implant models 34 (e.g., femur and tibia implantsin the context of the joint being a knee) and restored bone models 28are manually manipulated relative to each other by a person sitting infront of a computer 6 and visually observing the implant models 34 andrestored bone models 28 on the computer screen 9 and manipulating themodels 28, 34 via the computer controls 11. By superimposing the implantmodels 34 over the restored bone models 28, or vice versa, the jointsurfaces of the implant models 34 can be aligned or caused to correspondwith the joint surfaces of the restored bone models 28. By causing thejoint surfaces of the models 28, 34 to so align, the implant models 34are positioned relative to the restored bone models 28 such that the sawcut locations 30 and drill hole locations 32 can be determined relativeto the restored bone models 28.

In one embodiment, the POP process is generally or completely automated.For example, a computer program may manipulate computer generated 3Dimplant models 34 (e.g., femur and tibia implants in the context of thejoint being a knee) and restored bone models or planning bone models 28relative to each other to determine the saw cut and drill hole locations30, 32 relative to the restored bone models 28. The implant models 34may be superimposed over the restored bone models 28, or vice versa. Inone embodiment, the implant models 34 are located at point P′ (X_(P′),Y_(P′), Z_(P′)) relative to the origin (X₀, Y₀, Z₀), and the restoredbone models 28 are located at point P (X_(P), Y_(P), Z_(P)). To causethe joint surfaces of the models 28, 34 to correspond, the computerprogram may move the restored bone models 28 from point P (X_(P), Y_(P),Z_(P)) to point P′ (X_(P′), Y_(P′), Z_(P1)), or vice versa. Once thejoint surfaces of the models 28, 34 are in close proximity, the jointsurfaces of the implant models 34 may be shape-matched to align orcorrespond with the joint surfaces of the restored bone models 28. Bycausing the joint surfaces of the models 28, 34 to so align, the implantmodels 34 are positioned relative to the restored bone models 28 suchthat the saw cut locations 30 and drill hole locations 32 can bedetermined relative to the restored bone models 28. For more detailregarding a generally or completely automated system for the POPprocess, see U.S. patent application Ser. No. 12/563,809 to Park, whichis titled Arthroplasty System and Related Methods, was filed Sep. 21,2009, and is incorporated by reference in its entirety into thisDetailed Description.

While the preceding discussion regarding the POP process is given in thecontext of the POP process employing the restored bone models ascomputer generated 3D bone models, in other embodiments, the POP processmay take place without having to employ the 3D restored bone models, butinstead taking placing as a series of 2D operations. For more detailregarding a generally or completely automated system for the POP processwherein the POP process does not employ the 3D restored bone models, butinstead utilizes a series of 2D operations, see U.S. patent applicationSer. No. 12/546,545 to Park, which is titled Arthroplasty System andRelated Methods, was filed Aug. 24, 2009, and is incorporated byreference in its entirety into this Detailed Description.

As indicated in FIG. 1E, in one embodiment, the data 44 regarding thesaw cut and drill hole locations 30, 32 relative to point P′ (X_(P′),Y_(P′), Z_(P′)) is packaged or consolidated as the “saw cut and drillhole data” 44 [block 145]. The “saw cut and drill hole data” 44 is thenused as discussed below with respect to [block 150] in FIG. 1E.

As can be understood from FIG. 1D, the 2D images 16 employed to generatethe bone models 22 discussed above with respect to [block 110] of FIG.1C are also segmented along bone and cartilage boundaries to form boneand cartilage contour lines that are used to create computer generated3D bone and cartilage models (i.e., “arthritic models”) 36 of the bones18, 20 forming the patient's joint 14 [block 130]. Like theabove-discussed bone models 22, the arthritic models 36 are located suchthat point P is at coordinates (X_(P), Y_(P), Z_(P)) relative to theorigin (X₀, Y₀, Z₀) of the X-Y-Z axis [block 130]. Thus, the bone andarthritic models 22, 36 share the same location and orientation relativeto the origin (X₀, Y₀, Z₀). This position/orientation relationship isgenerally maintained throughout the process discussed with respect toFIGS. 1B-1E. Accordingly, movements relative to the origin (X₀, Y₀, Z₀)of the bone models 22 and the various descendants thereof (i.e., therestored bone models 28, bone cut locations 30 and drill hole locations32) are also applied to the arthritic models 36 and the variousdescendants thereof (i.e., the jig models 38). Maintaining theposition/orientation relationship between the bone models 22 andarthritic models 36 and their respective descendants allows the “saw cutand drill hole data” 44 to be integrated into the “jig data” 46 to formthe “integrated jig data” 48 employed by the CNC machine 10 tomanufacture the customized arthroplasty jigs 2.

Computer programs for creating the 3D computer generated arthriticmodels 36 from the segmented 2D images 16 (i.e., bone and cartilagecontour lines) include: Analyze from AnalyzeDirect, Inc., Overland Park,Kans.; Insight Toolkit, an open-source software available from theNational Library of Medicine Insight Segmentation and RegistrationToolkit (“ITK”), www.itk.org; 3D Slicer, an open-source softwareavailable from www.slicer.org; Mimics from Materialise, Ann Arbor,Mich.; and Paraview available at www.paraview.org. Some embodiments mayuse customized software such as OMSegmentation (renamed “PerForm” inlater versions), developed by OtisMed, Inc. The OMSegmentation softwaremay extensively use “ITK” and/or “VTK” (Visualization Toolkit fromKitware, Inc, available at www.vtk.org.). Also, some embodiments mayinclude using a prototype of OMSegmentation, and as such may utilizeInsightSNAP software.

Similar to the bone models 22, the arthritic models 36 depict the bones18, 20 in the present deteriorated condition with their respectivedegenerated joint surfaces 24, 26, which may be a result ofosteoarthritis, injury, a combination thereof, etc. However, unlike thebone models 22, the arthritic models 36 are not bone-only models, butinclude cartilage in addition to bone. Accordingly, the arthritic models36 depict the arthroplasty target areas 42 generally as they will existwhen the customized arthroplasty jigs 2 matingly receive thearthroplasty target areas 42 during the arthroplasty surgical procedure.

As indicated in FIG. 1D and already mentioned above, to coordinate thepositions/orientations of the bone and arthritic models 36, 36 and theirrespective descendants, any movement of the restored bone models 28 frompoint P to point P′ is tracked to cause a generally identicaldisplacement for the “arthritic models” 36 [block 135].

As depicted in FIG. 1D, computer generated 3D surface models 40 of thearthroplasty target areas 42 of the arthritic models 36 are importedinto computer generated 3D arthroplasty jig models 38 [block 140]. Thus,the jig models 38 are configured or indexed to matingly receive thearthroplasty target areas 42 of the arthritic models 36. Jigs 2manufactured to match such jig models 38 will then matingly receive thearthroplasty target areas of the actual joint bones during thearthroplasty surgical procedure.

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is a manual process. The 3D computergenerated models 36, 38 are manually manipulated relative to each otherby a person sitting in front of a computer 6 and visually observing thejig models 38 and arthritic models 36 on the computer screen 9 andmanipulating the models 36, 38 by interacting with the computer controls11. In one embodiment, by superimposing the jig models 38 (e.g., femurand tibia arthroplasty jigs in the context of the joint being a knee)over the arthroplasty target areas 42 of the arthritic models 36, orvice versa, the surface models 40 of the arthroplasty target areas 42can be imported into the jig models 38, resulting in jig models 38indexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. Point P′ (X_(P′), Y_(P′), Z_(P′)) can also beimported into the jig models 38, resulting in jig models 38 positionedand oriented relative to point P′ (X_(P′), Y_(P′), Z_(P′)) to allowtheir integration with the bone cut and drill hole data 44 of [block125].

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is generally or completely automated, asdisclosed in U.S. patent application Ser. No. 11/959,344 to Park, whichis entitled System and Method for Manufacturing Arthroplasty Jigs, wasfiled Dec. 18, 2007 and is incorporated by reference in its entiretyinto this Detailed Description. For example, a computer program maycreate 3D computer generated surface models 40 of the arthroplastytarget areas 42 of the arthritic models 36. The computer program maythen import the surface models 40 and point P′ (X_(P′), Y_(P′), Z_(P′))into the jig models 38, resulting in the jig models 38 being indexed tomatingly receive the arthroplasty target areas 42 of the arthriticmodels 36. The resulting jig models 38 are also positioned and orientedrelative to point P′ (X_(P′), Y_(P′), Z_(P′)) to allow their integrationwith the bone cut and drill hole data 44 of [block 125].

In one embodiment, the arthritic models 36 may be 3D volumetric modelsas generated from a closed-loop process. In other embodiments, thearthritic models 36 may be 3D surface models as generated from anopen-loop process.

As indicated in FIG. 1E, in one embodiment, the data regarding the jigmodels 38 and surface models 40 relative to point P′ (X_(P′), Y_(P′),Z_(P′)) is packaged or consolidated as the “jig data” 46 [block 145].The “jig data” 46 is then used as discussed below with respect to [block150] in FIG. 1E.

As can be understood from FIG. 1E, the “saw cut and drill hole data” 44is integrated with the “jig data” 46 to result in the “integrated jigdata” 48 [block 150]. As explained above, since the “saw cut and drillhole data” 44, “jig data” 46 and their various ancestors (e.g., models22, 28, 36, 38) are matched to each other for position and orientationrelative to point P and P′, the “saw cut and drill hole data” 44 isproperly positioned and oriented relative to the “jig data” 46 forproper integration into the “jig data” 46. The resulting “integrated jigdata” 48, when provided to the CNC machine 10, results in jigs 2: (1)configured to matingly receive the arthroplasty target areas of thepatient's bones; and (2) having cut slots and drill holes thatfacilitate preparing the arthroplasty target areas in a manner thatallows the arthroplasty joint implants to generally restore thepatient's joint line to its pre-degenerated state.

As can be understood from FIGS. 1A and 1E, the “integrated jig data” 44is transferred from the computer 6 to the CNC machine 10 [block 155].Jig blanks 50 are provided to the CNC machine 10 [block 160], and theCNC machine 10 employs the “integrated jig data” to machine thearthroplasty jigs 2 from the jig blanks 50.

For a discussion of example customized arthroplasty cutting jigs 2capable of being manufactured via the above-discussed process, referenceis made to FIGS. 1F-11. While, as pointed out above, the above-discussedprocess may be employed to manufacture jigs 2 configured forarthroplasty procedures involving knees, elbows, ankles, wrists, hips,shoulders, vertebra interfaces, etc., the jig examples depicted in FIGS.1F-1I are for total knee replacement (“TKR”) or partial knee replacement(“PKR”) procedures. Thus, FIGS. 1F and 1G are, respectively, bottom andtop perspective views of an example customized arthroplasty femur jig2A, and FIGS. 1H and 11 are, respectively, bottom and top perspectiveviews of an example customized arthroplasty tibia jig 2B.

As indicated in FIGS. 1F and 1G, a femur arthroplasty jig 2A may includean interior side or portion 100 and an exterior side or portion 102.When the femur cutting jig 2A is used in a TKR or PKR procedure, theinterior side or portion 100 faces and matingly receives thearthroplasty target area 42 of the femur lower end, and the exteriorside or portion 102 is on the opposite side of the femur cutting jig 2Afrom the interior portion 100.

The interior portion 100 of the femur jig 2A is configured to match thesurface features of the damaged lower end (i.e., the arthroplasty targetarea 42) of the patient's femur 18. Thus, when the target area 42 isreceived in the interior portion 100 of the femur jig 2A during the TKRor PKR surgery, the surfaces of the target area 42 and the interiorportion 100 match.

The surface of the interior portion 100 of the femur cutting jig 2A ismachined or otherwise formed into a selected femur jig blank 50A and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged lower end or target area 42 of the patient's femur 18.

As indicated in FIGS. 1H and 11, a tibia arthroplasty jig 2B may includean interior side or portion 104 and an exterior side or portion 106.When the tibia cutting jig 2B is used in a TKR or PKR procedure, theinterior side or portion 104 faces and matingly receives thearthroplasty target area 42 of the tibia upper end, and the exteriorside or portion 106 is on the opposite side of the tibia cutting jig 2Bfrom the interior portion 104.

The interior portion 104 of the tibia jig 2B is configured to match thesurface features of the damaged upper end (i.e., the arthroplasty targetarea 42) of the patient's tibia 20. Thus, when the target area 42 isreceived in the interior portion 104 of the tibia jig 2B during the TKRor PKR surgery, the surfaces of the target area 42 and the interiorportion 104 match.

The surface of the interior portion 104 of the tibia cutting jig 2B ismachined or otherwise formed into a selected tibia jig blank 50B and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged upper end or target area 42 of the patient's tibia 20.

II. Overview of Segmentation Process

A. Automatic Segmentation of Scanner Modality Image Data to Generate 3DSurface Model of a Patient's Bone

In one embodiment as mentioned above, the 2D images 16 of the patient'sjoint 14 as generated via the imaging system 8 (see FIG. 1A and [block100] of FIG. 1B) are segmented or, in other words, analyzed to identifythe contour lines of the bones and/or cartilage surfaces that are ofsignificance with respect to generating 3D models 22, 36, as discussedabove with respect to [blocks 110 and 130] of FIGS. 1C and 1D.Specifically, a variety of image segmentation processes may occur withrespect to the 2D images 16 and the data associated with such 2D images16 to identify contour lines that are then compiled into 3D bone models,such as bone models 22 and arthritic models 36. A variety of processesand methods for performing image segmentation are disclosed in theremainder of this Detailed Description.

The imager 8 typically generates a plurality of image slices 16 viarepetitive imaging operations. Depending on whether the imager 8 is aMRI or CT imager, each image slice will be a MRI or CT slice. As shownin FIG. 2A, the image slice may depict the cancellous bone 200, thecortical bone 202 surrounding the cancellous bone, and the articularcartilage lining portions of the cortical bone 202 of an object ofinterest of a joint, e.g., a femur 204 in a patient's knee joint 14. Theimage may further depict the cancellous bone 206, the cortical bone 208of another object of interest in the joint, e.g., a tibia 210 of theknee joint 14. In one embodiment, each image slice 16 may be atwo-millimeter 2D image slice.

One embodiment may automatically segment one or more features ofinterest (e.g., bones) present in MRI or CT scans of a patient joint,e.g., knee, hip, elbow, etc. A typical scan of a knee joint mayrepresent approximately a 100-millimeter by 150-millimeter by150-millimeter volume of the joint and may include about 40 to 80 slicestaken in sagittal planes. A sagittal plane is an imaginary plane thattravels from the top to the bottom of the object (e.g., the human body),dividing it into medial and lateral portions. It is to be appreciatedthat a large inter-slice spacing may result in voxels (volume elements)with aspect ratios of about one to seven between the resolution in thesagittal plane (e.g., the y z plane) and the resolution along the x axis(i.e., each scan slice lies in the yz plane with a fixed value of x).For example, a two-millimeter slice that is 150-millimeters by150-millimeters may be comprised of voxels that are approximately0.3-millimeter by 0.3-millimeter by 2-millimeters (for a 512 by 512image resolution in the sagittal plane).

In one embodiment, each slice may be a gray scale image with aresolution of 512 by 512 voxels where the voxel value represents thebrightness (intensity) of the voxel. The intensity may be stored as a16-bit integer resulting in an intensity range from 0 to 65,535, where 0may represent black and 65,535 may represent white. The intensity ofeach voxel typically represents the average intensity of the voxelvolume. Other embodiments may employ scans having higher or lowerresolutions in the sagittal plane, different inter-slice spacing, orimages where the intensity may be represented by a 24 bit vector (e.g.,eight bits each for a red component, green component and bluecomponent). Additionally, other embodiments may store intensity valuesas 32-bit signed integers or floating point values.

Typical MRI and CT scan data generally provide images where parts of abone boundary of interest may be well defined while other parts of thebone boundary may be difficult to determine due to voxel volumeaveraging, the presence of osteophyte growth, the presence of tissuehaving similar image intensities in neighboring areas to the object tobe segmented, amongst other things. Such poor definition of parts of thebone boundary in the images may cause traditional automated segmentationtechniques to fail. For example, FIG. 2A depicts regions 212 within aslice where an object boundary may not be visible due to neighboringtissue having about the same intensity as the feature of interest.Depicted in FIG. 2B are regions 214 that may be extended into the slicefrom adjacent slices due to a high voxel aspect ratio. Depicted in FIG.2C is a region 216 of the bone boundary 218 that may disappear or loseregularity when the bone boundary 218 is approximately tangent to theslice.

One embodiment may employ image segmentation techniques using a goldentemplate to segment bone boundaries and provide improved segmentationresults over traditional automated segmentation techniques. Suchtechniques may be used to segment an image when similarity betweenpixels within an object to be identified may not exist. That is, thepixels within a region to be segmented may not be similar with respectto some characteristic or computed property such as a color, intensityor texture that may be employed to associate similar pixels intoregions. Instead, a spatial relationship of the object with respect toother objects may be used to identify the object of interest. In oneembodiment, a 3D golden template of a feature of interest to besegmented may be used during the segmentation process to locate thetarget feature in a target scan. For example, when segmenting a scan ofa knee joint, a typical 3D image of a known good femur (referred to as agolden femur template) may be used to locate and outline (i.e., segment)a femur in a target scan.

Generally, much of the tissues surrounding the cancellous and corticalmatter of the bone to be segmented may vary from one MRI scan to anotherMRI scan. This may be due to disease and/or patient joint position(e.g., a patient may not be able to straighten the joint of interestbecause of pain). By using surrounding regions that have a stableconnection with the bone (e.g., the grown golden and boundary goldenregions of the template as described in more detail below), theregistration may be improved. Additionally, use of these regions allowsthe bone geometry of interest to be captured during the segmentationrather than other features not of interest. Further, the segmentationtakes advantage of the higher resolution of features of interest incertain directions of the scan data through the use of a combination of2D and 3D techniques, that selectively increases the precision of thesegmentation as described in more detail below with respect to refiningthe bone registration using an artificially generated image.

The segmentation method employed by one embodiment may accommodate avariety of intensity gradients across the scan data. FIGS. 3A-C depictintensity gradients (i.e., the intensity varies non-uniformly across theimage) in slices (an intensity gradient that is darker on the top andbottom as depicted in FIG. 3A, an intensity gradient that is darker onthe bottom as depicted in FIG. 3B, and an intensity gradient 220 that isbrighter on the sides as depicted in FIG. 3C) that may be segmented byone embodiment. Further, the embodiment generally does not requireapproximately constant noise in the slices to be segmented. Theembodiment may accommodate different noise levels, e.g., high noiselevels as depicted in FIG. 4A as well as low noise levels as depicted inFIG. 4B. The decreased sensitivity to intensity gradients and noiselevel typically is due to image registration techniques using a goldentemplate, allowing features of interest to be identified even though thefeature may include voxels with differing intensities and noise levels.

Segmentation generally refers to the process of partitioning a digitalimage into multiple regions (e.g., sets of pixels for a 2D image or setsof voxels in a 3D image). Segmentation may be used to locate features ofinterest (bones, cartilage, ligaments, etc.) and boundaries (lines,curves, etc. that represent the bone boundary or surface) in an image.In one embodiment, the output of the automatic segmentation of the scandata may be a set of images (scan slices 16) where each image 16includes a set of extracted closed contours representing bone outlinesthat identify respective bone location and shape for bones of interest(e.g., the shape and location of the tibia and femur in the scan data ofa knee joint). The generation of a 3D model correspondent to the aboveclosed contours may be additionally included into the segmentationprocess. The automatic or semi-automatic segmentation of a joint, usingimage slices 16 to create 3D models (e.g., bone models 22 and arthriticmodels 36) of the surface of the bones in the joint, may reduce the timerequired to manufacture customized arthroplasty cutting jigs 2. It is tobe appreciated that certain embodiments may generate open contours ofthe bone shapes of interest to further reduce time associated with theprocess.

In one embodiment, scan protocols may be chosen to provide gooddefinition in areas where precise geometry reconstruction is requiredand to provide lower definition in areas that are not as important forgeometry reconstruction. The automatic or semi-automatic imagesegmentation of one embodiment employs components whose parameters maybe tuned for the characteristics of the image modality used as input tothe automatic segmentation and for the features of the anatomicalstructure to be segmented, as described in more detail below.

In one embodiment, a General Electric 3T MRI scanner may be used toobtain the scan data. The scanner settings may be set as follows: pulsesequence: FRFSE-XL Sagittal PD; 3 Pane Locator-Scout Scan Thickness:4-millimeters; Imaging Options: TRF, Fast, FR; Gradient Mode: Whole; TE:approximately 31; TR: approximately 2100; Echo Train Length: 8;Bandwidth: 50 Hz; FOV: 16 centimeters, centered at the joint line; PhaseFOV: 0.8 or 0.9; Slice Thickness: 2 millimeters; Spacing: Interleave;Matrix: 384×192; NEX: 2; Frequency: SI; and Phase Correct: On. It is tobe appreciated that other scanners and settings may be used to generatethe scan data.

Typically, the voxel aspect ratio of the scan data is a function of howmany scan slices may be obtained while a patient remains immobile. Inone embodiment, a two-millimeter inter-slice spacing may be used duringa scan of a patient's knee joint. This inter-slice spacing providessufficient resolution for constructing 3D bone models of the patient'sknee joint, while allowing sufficiently rapid completion of scan beforethe patient moves.

FIG. 5 depicts a MRI scan slice that illustrates image regions wheregood definition may be needed during automatic segmentation of theimage. Typically, this may be areas where the bones come in contactduring knee motion, in the anterior shaft area next to the joint andareas located at about a 10- to 30-millimeter distance from the joint.Good definition may be needed in regions 230 of the tibia 232 andregions 234 of the femur 236. Regions 238 depict areas where the tibiais almost tangent to the slice and boundary information may be lost dueto voxel volume averaging.

Voxel volume averaging may occur during the data acquisition processwhen the voxel size is larger than a feature detail to be distinguished.For example, the detail may have a black intensity while the surroundingregion may have a white intensity. When the average of the contiguousdata enclosed in the voxel is taken, the average voxel intensity valuemay be gray. Thus, it may not be possible to determine in what part ofthe voxel the detail belongs.

Regions 240 depict areas where the interface between the cortical boneand cartilage is not clear (because the intensities are similar), orwhere the bone is damaged and may need to be restored, or regions wherethe interface between the cancellous bone and surrounding region may beunclear due to the presence of a disease formation (e.g., an osteophytegrowth which has an image intensity similar to the adjacent region).

FIG. 6 depicts a flowchart illustrating one method for automatic orsemi-automatic segmentation of Femur and Tibia Planning models of animage modality scan (e.g., an MRI scan) of a patient's knee joint.Initially, operation 250 obtains a scan of the patient's knee joint. Inone embodiment, the scan may include about 50 sagittal slices. Otherembodiments may use more or fewer slices. Each slice may be a gray scaleimage having a resolution of 512 by 512 voxels. The scan may representapproximately a 100-millimeter by 150-millimeter by 150-millimetervolume of the patient's knee. While the invention will be described foran MRI scan of a knee joint, this is by way of illustration and notlimitation. The invention may be used to segment other types of imagemodality scans such as computed tomography (CT) scans, ultrasound scans,positron emission tomography (PET) scans, etc., as well as other jointsincluding, but not limited to, hip joints, elbow joints, etc. Further,the resolution of each slice may be higher or lower and the images maybe in color rather than gray scale. It is to be appreciated thattransversal or coronal slices may be used in other embodiments.

After operation 250 obtains scan data (e.g., scan images 16) generatedby imager 8, operation 252 may be performed to segment the femur data ofthe scan data. During this operation, the femur may be located andspline curves 270 may be generated to outline the femur shape or contourlines in the scan slices, as depicted in FIGS. 7A-7K. It should beappreciated that one or more spline curves may be generated in eachslice to outline the femur contour depending on the shape and curvatureof the femur as well as the femur orientation relative to the slicedirection.

Next, in operation 254, a trained technician may verify that thecontours of the femur spline curves generated during operation 252follow the surface of the femur bone. The technician may determine thata spline curve does not follow the bone shape in a particular slice. Forexample, FIG. 8 depicts an automatically generated femur spline curve274. The technician may determine that the curve should be enlarged inthe lower left part 276 of the femur. There may be various reasons whythe technician may decide that the curve needs to be modified. Forexample, a technician may want to generate a pre-deteriorated boneshape, yet the bone may be worn out in this region and may needreconstruction. The technician may determine this by examining theoverall 3D shape of the segmented femur and also by comparing lateraland medial parts of the scan data. The segmented region of the slice maybe enlarged by dragging one or more control points 278 located on thespline curve 274 to adjust the curve to more closely follow the femurboundary as determined by the technician, as shown by adjusted curve280. The number of control points on a spline curve may be dependent onthe curve length and curvature variations. Typically, 10-25 controlpoints may be associated with a spline curve for spline modification.

Once the technician is satisfied with all of the femur spline curves inthe scan slices, operation 256 generates a watertight triangular meshgeometry from the femur segmentation that approximates the 3D surface ofthe femur. The mesh closely follows the femur spline curves 270 andsmoothly interpolates between them to generate a 3D surface model of thefemur. FIG. 9 depicts typical 3D mesh geometry 290 of a target femurgenerated by one embodiment. Such a 3D model may be a 3D surface modelor 3D volume model resulting from open-loop contour lines or closed loopcontour lines, respectively. In one embodiment, such a 3D model asdepicted in FIG. 9 may be a bone model 22 or an arthritic model 36.

After operation 256, operation 258 may be performed to segment the tibiadata in the scan data. During this operation, the tibia is located andspline curves may be generated to locate and outline the shape of thetibia found in the scan slices, as depicted by tibia spline curves 272in FIGS. 7A-7K. It should be appreciated that one or more spline curvesmay be generated in each slice to outline the tibia depending on theshape and curvature of the tibia as well as the tibia orientationrelative to the slice direction.

Next, in operation 260, the technician may verify the tibia splinecurves generated during operation 258. The technician may determine thata spline curve does not follow the tibia in a particular slice. Forexample, referring back to FIG. 8, an automatically generated tibiaspline curve 282 is depicted that may not follow the tibia in the rightpart of the tibia due to the presence of an osteophyte growth 284. Thepresence of the osteophyte growth 284 may be determined by examiningneighboring slices. In this case, the segmented region may be reduced bydragging one or more control points 286 located on the spline curve tomodify the tibia spline curve 282 to obtain the adjusted tibia splinecurve 288. As previously discussed, each spline curve may haveapproximately 10-25 control points depending on the length and curvaturevariation of the spline curve.

When the purpose of the segmentation is generating bone models that willbe shown to a surgeon in the images where they are overlapped byimplants, a technician will not need to restore the segmentation modelto its pre-deteriorated bone shape, and thus will not need to spend timeon adjusting splines to follow the pre-deteriorated bone shape. Alsothere is no need to get highly precise segmentation in the bone areasthat are to be replaced with implant. So there is no need to spend timeon adjusting the non-perfect curves in the “to be replaced” areas.

Once the technician is satisfied with all of the tibia spline curves inthe scan slices, operation 262 generates a watertight triangular meshgeometry from the tibia segmentation. The mesh closely follows thespline curves and smoothly interpolates between them to generate a 3Dsurface model of the tibia. FIG. 10 depicts a typical 3D mesh geometry292 of a target tibia generated by one embodiment. Such a 3D model maybe a 3D surface model or 3D volume model resulting from open-loopcontour lines or closed loop contour lines, respectively. In oneembodiment, such a 3D model as depicted in FIG. 10 may be a bone model22 or an arthritic model 36.

Because the objects to be located in the scan data typically cannot besegmented by grouping similar voxels into regions, a golden templaterepresentative of a typical size and shape of the feature of interestmay be employed during the segmentation process to locate the targetfeature of interest.

FIG. 11 depicts a flowchart illustrating one method for generating agolden template. The method will be described for generating a goldentemplate of a tibia by way of illustration and not limitation. Themethod may be used to generate golden templates of other bonesincluding, but not limited to a femur bone, a hip bone, etc.

Initially, operation 300 obtains a scan of a tibia that is not damagedor diseased. The appropriate tibia scan may be chosen by screeningmultiple MRI tibia scans to locate a MRI tibia scan having a tibia thatdoes not have damaged cancellous and cortical matter (i.e., no damage intibia regions that will be used as fixed images to locate acorresponding target tibia in a target scan during segmentation), whichhas good MRI image quality, and which has a relatively average shape,e.g., the shaft width relative to the largest part is not out ofproportion (which may be estimated by eye-balling the images). Thistibia scan data, referred to herein as a golden tibia scan, may be usedto create a golden tibia template. It is to be appreciated that severalMRI scans of a tibia (or other bone of interest) may be selected, atemplate generated for each scan, statistics gathered on the successrate when using each template to segment target MRI scans, and the onewith the highest success rate selected as the golden tibia template.

In other embodiments, a catalog of golden models may be generated forany given feature, with distinct variants of the feature depending onvarious patient attributes, such as (but not limited to) weight, height,race, gender, age, and diagnosed disease condition. The appropriategolden mesh would then be selected for each feature based on a givenpatient's characteristics.

Then, in operation 302 the tibia is segmented in each scan slice. Eachsegmentation region includes the cancellous matter 322 and corticalmatter 324 of the tibia, but excludes any cartilage matter to form agolden tibia region, outlined by a contour curve 320, as depicted inFIG. 12A.

Next, operation 304 generates a golden tibia mesh 340 from theaccumulated golden tibia contours of the image slices, as illustrated inFIG. 13A.

Next, operation 306 increases the segmented region in each slice bygrowing the region to include boundaries between the tibia and adjacentstructures where the contact area is generally relatively stable fromone MRI scan to another MRI scan. This grown region may be referred toherein as a grown golden tibia region, outlined by contour curve 328, asdepicted in FIG. 12A.

The grown golden region may be used to find the surface that separatesthe hard bone (cancellous and cortical) from the outside matter(cartilage, tendons, water, etc.). The changes in voxel intensities whengoing from inside the surface to outside of the surface may be used todefine the surface. The grown golden region may allow the registrationprocess to find intensity changes in the target scan that are similar tothe golden template intensity changes near the surface. Unfortunately,the golden segmentation region does not have stable intensity changes(e.g., near the articular surface) or may not have much of an intensitychange. Thus, the grown region typically does not include such regionsbecause they do not provide additional information and may slow down theregistration due to an increased number of points to be registered.

Finally, use of a grown golden region may increase the distance wherethe metric function detects a feature during the registration process.When local optimization is used, the registration may be moved in aparticular direction only when a small movement in that directionimproves the metric function. When a golden template feature is fartheraway from the corresponding target bone feature (e.g., when there is asignificant shape difference), the metric typically will not move towardthat feature. Use of the larger grown region may allow the metric todetect the feature and move toward it.

Next, operation 308 cuts off most of the inner part of the grown goldentibia region to obtain a boundary golden tibia region 330 depicted inFIG. 12A. The boundary golden tibia region 330 is bounded on the insideby contour curve 332 and the outside by contour curve 328.

The boundary region may be used to obtain a more precise registration ofthe target bone by using the interface from the cancellous bone to thecortical bone. This may be done so that intensity variations in otherareas (e.g., intensity variations deep inside the bone) that may movethe registration toward wrong features and decrease the precision oflocating the hard bone surface are not used during the registration.

Then, operation 310 applies Gaussian smoothing with a standard deviationof two pixels to every slice of the golden tibia scan. In oneembodiment, a vtklmageGaussianSmooth filter (part of VisualizationToolkit, a free open source software package) may be used to perform theGaussian smoothing by setting the parameter “Standard Deviation” to avalue of two.

Then, operation 312 generates an anchor segmentation. The anchorsegmentation typically follows the original segmentation where the tibiaboundary is well defined in most MRI scans. In areas where the tibiaboundary may be poorly defined, but where there is another well-definedfeature close to the tibia boundary, the anchor segmentation may followthat feature instead. For example, in an area where a healthy bonenormally has cartilage, a damaged bone may or may not have cartilage. Ifcartilage is present in this damaged bone region, the bone boundaryseparates the dark cortical bone from the gray cartilage matter. Ifcartilage is not present in this area of the damaged bone, there may bewhite liquid matter next to the dark cortical bone or there may beanother dark cortical bone next to the damaged bone area. Thus, theinterface from the cortical bone to the outside matter in this region ofthe damaged bone typically varies from MRI scan to MRI scan. In suchareas, the interface between the cortical and the inner cancellous bonemay be used. These curves may be smoothly connected together in theremaining tibia areas to obtain the tibia anchor segmentation curve 358,depicted in FIG. 14A.

Then, operation 314 may determine three disjoint regions along theanchor segmentation boundary. Each of these regions is generally welldefined in most MRI scans. FIG. 14A depicts these three disjoint regionsfor a particular image slice. The first region 350, referred to hereinas the tibia InDark-OutLight region, depicts a region where the anchorsegmentation boundary separates the inside dark intensity corticalmatter voxels from the outside light intensity voxels. The second region352, referred to herein as the tibia InLight-OutDark region, depicts aregion where the boundary separates the inside light intensitycancellous matter voxels from the outside dark intensity cortical mattervoxels. Finally, region 354, referred to herein as the tibiaDark-in-Light region, depicts a region that has a very thin layer ofdark intensity cortical matter voxels along the boundary, but which haslight intensity matter voxels away from the boundary (i.e., on bothsides of the boundary). Generally, the other regions along the anchorsegmentation boundary vary from scan to scan or may not be clear in mostof the scans, as depicted by regions 356. Such regions may be anosteophyte growth with an arbitrary shape but which has about the sameintensity as the region next to it. Thus, such regions typically are notused as anchor regions in one embodiment of the invention.

Finally, operation 316 generates a mesh corresponding to the anchorsegmentation and also generates a mesh for each anchor region. FIG. 15Adepicts the anchor segmentation mesh 360, the InDark-OutLight anchorregion mesh 362, the InLight-OutDark anchor region mesh 364 and theDark-in-Light anchor region mesh 366 for the tibia. These 3D meshesmodel the surface of the golden tibia in the specified regions. It is tobe appreciated that the 3D meshes are distinct and generally are notcombined to create a composite mesh. These meshes may be used to createan artificial fixed image that is used during the registration processas described in more detail below.

A golden template of a femur may also be generated in a similar mannerusing the method depicted by FIG. 11. FIG. 12B depicts the golden femurregion, outlined by a contour curve 320A, the grown femur region,outlined by contour curve 328A, and the boundary golden femur region330A bounded on the inside by contour curve 332A and the outside bycontour curve 328A. FIG. 13B depicts the golden femur mesh 340A. FIG.14B depicts the femur anchor segmentation curve 358A, the femurInDark-OutLight region 350A and the femur InLight-OutDark region 352A.Finally, FIG. 15B depicts the anchor segmentation mesh 360A, theInDark-OutLight anchor region mesh 362A and the InLight-OutDark anchorregion mesh 364A for the femur.

FIG. 16 depicts a flowchart illustrating one method for performingautomatic segmentation (e.g., operation 252 or operation 258 of FIG. 6)of the scan data of a joint (e.g., a MRI scan of a knee joint) usinggolden template registration. The segmentation method may be used tosegment the femur (operation 252 of FIG. 6) and/or the tibia (operation258 of FIG. 6) in either the left or right knee. Different goldentemplate data may be used to segment the left tibia, right tibia, leftfemur or right femur. Additionally, other embodiments may segment otherjoints, including but not limited to, hip joints, elbow joints, by usingan appropriate golden template of the feature of interest to besegmented.

Initially, operation 370 maps the segmented 3D golden template andmarked regions (e.g., grown and boundary regions) to the target scandata using image registration techniques. This may be done to locate thecorresponding feature of interest in the target scan (e.g., a targetfemur or tibia). Registration transforms the template image coordinatesystem into the target coordinate system. This allows the template imageto be compared and/or integrated with the target image.

Next, operation 372 refines the registration near the feature (e.g., abone) boundary of interest. Anchor segmentation and anchor regions maybe used with a subset of 3D free-form deformations to move points withinthe plane of the slices (e.g., the yz plane) but not transversal (alongthe x axis) to the slices. Refinement of the initial registrationoperation may be necessary to correct errors caused by a high voxelaspect ratio. When a point from a golden template is mapped onto thetarget scan, it generally maps to a point between adjacent slices of thescan data. For example, if a translation occurs along the x direction,then the point being mapped may only align with a slice when thetranslation is a multiple of the inter-slice scan distance (e.g., amultiple of two-millimeters for an inter-slice spacing oftwo-millimeters). Otherwise, the point will be mapped to a point thatfalls between slices. In such cases, the intensity of the target scanpoint may be determined by averaging the intensities of correspondingpoints (voxels) in the two adjacent slices. This may further reduceimage resolution. Additionally, refinement of the initial registrationoperation may correct for errors due to unhealthy areas and/or limitedcontrast areas. That is, the golden template may be partially pulledaway from the actual bone boundary in diseased areas and/or minimalcontrast areas (e.g., toward a diseased area having a differentcontrast) during the initial registration operation.

Next, operation 374 generates a polygon mesh representation of thesegmented scan data. A polygon mesh typically is a collection ofvertices, edges, and faces that may define the surface of a 3D object.The faces may consist of triangles, quadrilaterals or other simpleconvex polygons. In one embodiment, a polygon mesh may be generated byapplying the registration transform found during operation 372 to allthe vertices of a triangle golden template mesh (i.e., the surface ofthe mesh is composed of triangular faces). It is to be appreciated thatthe cumulative registration transform typically represents the transformthat maps the golden template into the target MRI scan with minimalmisalignment error.

Finally, operation 376 generates spline curves that approximate theintersection of the mesh generated by operation 374 with the target MRIslices. Note that these spline curves may be verified by the technician(during operation 254 or operation 260 of FIG. 6).

FIG. 17 depicts a flowchart illustrating one method for mapping thesegmented golden femur template regions into the target scan using imageregistration techniques. Registration may be thought of as anoptimization problem with a goal of finding a spatial mapping thataligns a fixed image with a target image. Generally several registrationoperations may be performed, first starting with a coarse imageapproximation and a low-dimensional transformation group to find a roughapproximation of the actual femur location and shape. This may be doneto reduce the chance of finding wrong features instead of the femur ofinterest. For example, if a free-form deformation registration wasinitially used to register the golden femur template to the target scandata, the template might be registered to the wrong feature, e.g., to atibia rather than the femur of interest. A coarse registration may alsobe performed in less time than a fine registration, thereby reducing theoverall time required to perform the registration. Once the femur hasbeen approximately located using a coarse registration, finerregistration operations may be performed to more accurately determinethe femur location and shape. By using the femur approximationdetermined by the prior registration operation as the initialapproximation of the femur in the next registration operation, the nextregistration operation may find a solution in less time.

In one embodiment, each registration operation may employ a registrationframework 390 as depicted in FIG. 18. The registration framework 390 mayemploy an image similarity-based method. Such a method generallyincludes a transformation model T(X) 392, which may be applied tocoordinates of a fixed (or reference) image 394 (e.g., a golden femurtemplate) to locate their corresponding coordinates in a target image396 space (e.g., a MRI scan), an image similarity metric 398, whichquantifies the degree of correspondence between features in both imagespaces achieved by a given transformation, and an optimizer 400, whichtries to maximize image similarity (or minimize an opposite function) bychanging the parameters of the transformation model 392. An interpolator402 may be used to evaluate target image intensities at non-gridlocations (e.g., reference image points that are mapped to target imagepoints that lie between slices). Thus, a registration frameworktypically includes two input images, a transform, a metric, aninterpolator and an optimizer.

Referring again to FIG. 17, operation 380 may approximately register agrown femur region in a MRI scan using a coarse registrationtransformation. In one embodiment, this may be done by performing anexhaustive translation transform search on the MRI scan data to identifythe appropriate translation transform parameters that minimizestranslation misalignment of the reference image femur mapped onto thetarget femur of the target image. This coarse registration operationtypically determines an approximate femur position in the MRI scan.

A translational transform, translates (or shifts) all elements of animage by the same 3D vector. That is, the reference femur may be mappedinto the target image space by shifting the reference femur along one ormore axes in the target image space to minimize misalignment. Duringthis operation the reference femur is not rotated, scaled or deformed.In one embodiment, three parameters for the translation transformationmay be generated: one parameter for each dimension that specifies thetranslation for that dimension. The final parameters of the translationtransform minimizing the misalignment of the mapped reference femurimage coordinates into the target image space may be stored.

Next, operation 382 further refines the image registration determined byoperation 380. This may be done by approximately registering the grownfemur region of the reference golden template femur into the target MRIscan data using a similarity transformation. In one embodiment, asimilarity transformation may be performed in 3D space. The referencegolden femur region may be rotated in 3D, translated in 3D andhomogeneously scaled to map its coordinates into the target MRI scandata to minimize misalignment between the reference golden femur regionand the corresponding region in the target MRI scan. In someembodiments, a center of rotation may be specified so that both therotation and scaling operations are performed with respect to thespecified center of rotation. In one embodiment, a 3D similaritytransformation, specified by seven parameters, may be used. Oneparameter specifies the scaling factor, three parameters specify aversor that represents the 3D rotation and three parameters specify avector that represents the 3D translation in each dimension. A versor isa unit quaternion that provides a convenient mathematical notation forrepresenting orientations and rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference golden femur region onto the target MRI scan that is not farfrom the registration of the reference golden femur region onto thetarget MRI scan found in the previous operation 190 and used as theinitial starting approximation. Registering the grown golden femurregion may increase the distance where the metric function detects afeature during the registration process. When local optimization isused, the registration may be moved in a particular direction only whena small movement in that direction improves the metric function. When agolden femur template feature is farther away from the correspondingtarget femur feature (e.g., when there is a significant shapedifference), the metric typically will not move toward that feature. Useof the larger grown femur region may allow the metric to detect thefeature and move toward it.

After operation 382, operation 384 further refines the imageregistration of the golden femur into the target scan. In oneembodiment, an affine transformation may be used to register coordinatesof a boundary golden femur region of a golden femur template into thetarget MRI scan data. In one embodiment, the approximate femurregistration found during operation 382 may be used as the initialstarting approximation for the affine transformation.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe boundary golden femur region mapped into the target MRI scan datamay be stored.

Finally, operation 386 further refines the image registration of theboundary golden femur region. In one embodiment, a spline transformationmay be used to register the coordinates of the boundary golden femurregion into the MRI scan data (target image space). In one embodiment, a3D B-Spline deformable transformation may be employed and thetransformation found in operation 384 may be used as the initialtransformation values for the 3D B-Spline deformable transformation.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. For example, a 3D B-splinedeformable transform T may specify a 3D vector V(P) for every point P inthe original 3D space that is moved by T such that T:P→P+V(P).

In one embodiment, a B-Spline transformation may be specified with M×Nparameters, where M is the number of nodes in the B-Spline grid and N isthe dimension of the space. In one embodiment, a 3D B-Spline deformabletransformation of order three may be used to map every reference image3D point into the target MRI scan by a different 3D vector. The field ofthe vectors may be modeled using B-splines. Typically a grid J×K×L ofcontrol points may be specified where J, K, and L are parameters of thetransformation.

In one embodiment, splines of order three may be used with a grid 9×6×6of control points. That is, the transformation employs nine controlpoints in the medial/lateral direction (i.e., the x direction), and sixcontrol points in the other directions (i.e., y and z directions). Twocontrol points in each dimension (i.e., 2 of 9 in the x direction, 2 of6 in the y direction and 2 of 6 in the z direction) may be used tospecify boundary conditions. As such, the inner spline nodes may form agrid of size 7 by 4 by 4 and the boundary conditions increase the gridto size 9 by κ by 6. The parametric set for this transformation has adimension of 3×9×6×6=972 (i.e., each dimension may have a 9×6×6 grid ofcontrol points). The final parameters of the spline transformation thatminimizes the misalignment between the reference golden femur templateand the target MRI scan data may be stored. This may be referred to asthe cumulative femur registration transform herein.

FIG. 19 depicts a flowchart illustrating one method for mapping thesegmented golden tibia template regions into the target scan using imageregistration techniques. Generally several registration operations maybe performed, first starting with a coarse image approximation and alow-dimensional transformation group to find a rough approximation ofthe actual tibia location and shape. This may be done to reduce thechance of finding wrong features instead of the tibia of interest. Forexample, if a free-form deformation registration was initially used toregister the golden tibia template to the target scan data, the templatemight be registered to the wrong feature, e.g., to a femur rather thanthe tibia of interest. A coarse registration may also be performed inless time than a fine registration, thereby reducing the overall timerequired to perform the registration. Once the tibia has beenapproximately located using a coarse registration, finer registrationoperations may be performed to more accurately determine the tibialocation and shape. By using the tibia approximation determined by theprior registration operation as the initial approximation of the tibiain the next registration operation, the next registration operation mayfind a solution in less time.

In one embodiment, each registration operation may employ a registrationframework 390 as depicted in FIG. 18. The registration framework 390 mayemploy an image similarity-based method. Such a method generallyincludes a transformation model T(X) 392, which may be applied tocoordinates of a fixed (or reference) image 394 (e.g., a golden tibiatemplate) to locate their corresponding coordinates in a target image396 space (e.g., a MRI scan), an image similarity metric 398, whichquantifies the degree of correspondence between features in both imagespaces achieved by a given transformation, and an optimizer 400, whichtries to maximize image similarity by changing the parameters of thetransformation model 392. An interpolator 402 may be used to evaluatetarget image intensities at non-grid locations (i.e., reference imagepoints that are mapped to target image points that lie between slices).Thus, a registration framework typically includes two input images, atransform, a metric, an interpolator and an optimizer.

The automatic segmentation registration process will be described usingscan data that includes a right tibia bone. This is by way ofillustration and not limitation. Referring again to FIG. 19, operation410 may approximately register a grown tibia region in a MRI scan usinga coarse registration transformation. In one embodiment, this may bedone by performing an exhaustive translation transform search on the MRIscan data to identify the appropriate translation transform parametersthat minimizes translation misalignment of the reference image tibiamapped onto the target tibia of the target image. This coarseregistration operation typically determines an approximate tibiaposition in the MRI scan. During this operation, the tibia of thereference image may be overlapped with the target tibia of the targetimage using a translation transformation to minimize translationalmisalignment of the tibias.

A translational transform, translates (or shifts) an image by the same3D vector. That is, the reference tibia may be mapped into the targetimage space by shifting the reference tibia along one or more axes inthe target image space to minimize misalignment. During this operationthe reference tibia is not rotated, scaled or deformed. In oneembodiment, three parameters for the translation transformation may begenerated, one parameter for each dimension that specifies thetranslation for that dimension. The final parameters of the translationtransform minimizing the misalignment of the mapped reference tibiaimage coordinates into the target image space may be stored.

Next, operation 412 further refines the image registration determined byoperation 410. This may be done by approximately registering the growntibia region of the reference golden tibia template into the target MRIscan data using a similarity transformation. In one embodiment, asimilarity transformation may be performed in 3D space. The referencegolden tibia region may be rotated in 3D, translated in 3D andhomogeneously scaled to map its coordinates into the target MRI scandata to minimize misalignment between the reference golden tibia regionand the corresponding region in the target MRI scan. In someembodiments, a center of rotation may be specified so that both therotation and scaling operations are performed with respect to thespecified center of rotation. In one embodiment, a 3D similaritytransformation, specified by seven parameters, may be used. Oneparameter specifies the scaling factor, three parameters specify aversor that represents the 3D rotation and three parameters specify avector that represents the 3D translation in each dimension. A versor isa unit quaternion that provides a convenient mathematical notation forrepresenting orientations and rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference golden tibia region onto the target MRI scan that is not farfrom the registration of the reference golden tibia region onto thetarget MRI scan found in the previous operation 410 and used as theinitial starting approximation. Registering the grown golden tibiaregion may increase the distance where the metric function detects afeature during the registration process. When local optimization isused, the registration may be moved in a particular direction only whena small movement in that direction improves the metric function. When agolden tibia template feature is farther away from the correspondingtarget tibia feature (e.g., when there is a significant shapedifference), the metric typically will not move toward that feature. Useof the larger grown tibia region may allow the metric to detect thefeature and move toward it.

After operation 412, operation 414 further refines the imageregistration. In one embodiment, an affine transformation may be used toregister coordinates of a boundary golden tibia region of a golden tibiatemplate into the target MRI scan data. In one embodiment, theapproximate tibia registration found during operation 412 may be used asthe initial starting approximation for the affine transformation.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe boundary golden tibia region mapped into the target MRI scan datamay be stored.

Finally, operation 416 further refines the image registration of theboundary golden tibia region. In one embodiment, a spline transformationmay be used to register the coordinates of the boundary golden tibiaregion into the MRI scan data (target image space). In one embodiment, a3D B-Spline deformable transformation may be employed and thetransformation found in operation 414 may be used as the initialtransformation values for the 3D B-Spline deformable transformation.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. In one embodiment, aB-Spline transformation may be specified with M×N parameters, where M isthe number of nodes in the B-Spline grid and N is the dimension of thespace. In one embodiment, a 3D B-Spline deformable transformation oforder three may be used to map every reference image 3D point into thetarget MRI scan by a different 3D vector. The field of the vectors maybe modeled using B-splines. Typically a grid J×K×L of control points maybe specified where J, K, and L are parameters of the transformation.

In one embodiment, splines of order three may be used with a grid 9×6×6of control points. That is, the transformation employs nine controlpoints in the medial/lateral direction (i.e., the x direction, and sixcontrol points in the other directions (i.e., the y and z directions).Two control points in each dimension (i.e., 2 of 9 in the x direction, 2of 6 in the y direction and 2 of 6 in the z direction) may be used tospecify boundary conditions. As such, the inner spline nodes may form agrid of size 7 by 4 by 4 and the boundary conditions increase the gridto size 9 by 6 by 6. The parametric set for this transformation has adimension of 3×9×6×6=972. The final parameters of the splinetransformation that minimizes the misalignment between the referencegolden tibia template and the target MRI scan data may be stored. Thismay be referred to as the cumulative tibia registration transformherein.

The shape of the tibia may vary more from patient to patient than doesthe shape of the femur. As a result, the affine transformation may notprovide a close enough registration of the golden tibia template to thetarget tibia in the target scan. This may cause the Splinetransformation to find a local optimum that may be far from the actualtibia in some areas. In one embodiment, an additional registrationoperation between the affine transform and spline transform operationsmay be performed to more closely align the golden tibia and the targettibia, allowing the spline transform to converge to the correct localoptimum rather than a nearby (but wrong) local optimum.

The class of transforms utilized generally should allow more flexibility(or degrees of freedom) than the Affine transform and less flexibilitythan the B-spline transforms. The number of degrees of freedom generallyis equal to the number of transform parameters. In one embodiment, aclass of transforms with more than 12 parameters and less than 3×9×6×6parameters may be used. For example, a B-spline transform with fewercontrol points (than used in the subsequent spline transform) may beused for the additional transform operation. Alternatively, thedeformations may be modeled using quadric rather than cubic functions.

In another embodiment, several golden tibia templates may be used thatrepresent typical tibia variations, e.g., golden tibia templates forvarum, valgum and normal tibia. In one embodiment, each of the goldentibia templates may be used during the translation, similarity andaffine transform registration operations to find the template thatprovides the best match (e.g., best correlation) in the affine transformregistration operation. This template may then be used in the remainingregistration operations.

Finally, in one embodiment, the tibia registration may be improved byperforming the tibia segmentation after the femur segmentation andadding a restriction on the tibia registration transformations such thatthe tibia may not penetrate the femur. In one embodiment, this may beimplemented by introducing a penalty for the penetration. In the targetMRI all the voxels that lie inside the femur splines may be marked. Themetric functions, described in more detail below, that are used in theregistration operations may be modified to include a penalty term. Thepenalty term may be computed by selecting a set of points on theboundary of the golden template segmentation, applying a transform tothe set of points (in a similar way as the transform is applied to thesample points used in the correlation computations), determining if atransformed sample point falls into any of the marked voxels, and addinga large value to the penalty term for each transformed sample point thatfalls into any of the marked voxels.

In each of the above registration operations, a metric may be used toquantify the degree of correspondence between features in both thereference image and target image achieved by a given transformation. Inone embodiment, the metric quantitatively measures how well thetransformed golden template image fits the target image (e.g., a targetMRI scan) and may compare the gray-scale intensity of the images using aset of sample points in the golden template region to be registered.

FIG. 20 depicts a flowchart illustrating one method for computing themetric used by the registration operations described above. For aparticular registration operation, the metric may be computed in thesame way, but the metric may have different parameters specified for theparticular registration operation. The metric may be referred to hereinas “local correlation in sample points.” Initially, operation 420selects a set of sample points in the golden template region to beregistered.

For the translation and similarity transformations, the sample pointsmay be selected as follows. Initially, a rectilinear grid of L×M×N thatcovers the whole bone in 3D space may be used. L, M, and N may vary fromone to 16. In one embodiment, an eight by eight grid in every imageslice may be used to select uniform sample points in the grown goldenregion of the golden template. For each grid cell, the first samplepoint is selected. If the sample point falls within the grown goldenregion, it is used. If the sample point falls outside the golden region,it is discarded.

For the affine and spline transformations, the sample points may bedetermined by randomly selecting one out of every 32 points in theboundary golden region of the MRI slice.

Next, operation 422 groups the selected points into buckets. In oneembodiment, buckets may be formed as follows. First, the 3D space may besubdivided into cells using a rectilinear grid. Sample points thatbelong to the same cell are placed in the same bucket. It should benoted that sample points may be grouped into buckets to compensate fornon-uniform intensities in the MRI scan.

For example, MRI scan data may be brighter in the middle of the imageand darker towards the edges of the image. This brightness gradienttypically is different for different scanners and may also depend onother parameters including elapsed time since the scanner was lastcalibrated. Additionally, high aspect ratio voxels typically result invoxel volume averaging. That is, cortical bone may appear very dark inareas where its surface is almost perpendicular to the slice andgenerally will not be averaged with nearby tissues. However, corticalbone may appear as light gray in the areas where its surface is almosttangent to the slice and generally may be averaged with a large amountof nearby tissues.

Next, operation 424 sub-samples the target MRI slice. Sub-sampling thetarget space generally has the effect of smoothing the metric function.This may remove tiny local minima such that the local minimizationalgorithm converges to a deeper minimum. In one embodiment, duringoperations 410 and 412 (of FIG. 19), each slice may be sub-sampled withan eight by eight grid. During operations 414 and 416 (of FIG. 19), eachslice may be sub-sampled with a four by four grid. That is, during thesub-sampling, one point from every grid cell may be selected (e.g., thefirst point) and the remaining points in the grid cells may bediscarded.

Next, operation 426 computes a correlation of the intensities of thepoints in each bucket and their corresponding points in the target MRIscan (after mapping). The correlation (NC) metric may be expressed as:

${{NC}\left( {A,B} \right)} = {\frac{\sum\limits_{i = 1}^{N}\;{A_{i}B_{i}}}{\sqrt{\left( {\sum\limits_{i = 1}^{N}\;{A_{i}}^{2}} \right)\left( {\sum\limits_{i = 1}^{N}\;{B_{i}}^{2}} \right)}}\frac{{{N\Sigma A}_{i}B_{i}} - {{\Sigma A}_{i}{\Sigma B}_{i}}}{\sqrt{{{N\Sigma A}_{i}}^{2} - \left( {\Sigma A}_{i} \right)^{2}}\sqrt{{{N\Sigma B}_{i}}^{2} - \left( {\Sigma B}_{i} \right)^{2}}}}$

where A_(i) is the intensity in the i^(th) voxel of image A, B_(i) isthe intensity in the corresponding i^(th) voxel of image B and N is thenumber of voxels considered, and the sum is taken from i equals one toN. It should be appreciated that the metric may be optimal when imagedifferences are minimized (or when the correlation of image similaritiesis maximized). The NC metric generally is insensitive to intensityshifts and to multiplicative factors between the two images and mayproduce a cost function with sharp peaks and well defined minima.

Finally, operation 428 averages the correlations computed in everybucket with weights proportional to the number of sample points in thebucket.

It is to be appreciated that the above process for computing the metricmay compensate for non-uniform intensities, for example, those describedabove with respect to FIGS. 3A-3C, in the MRI scan data.

During the registration process, an optimizer may be used to maximizeimage similarity between the reference image and target image byadjusting the parameters of a given transformation model to adjust thelocation of reference image coordinates in the target image. In oneembodiment, the optimizer for a registration operation may use thetransformed image (e.g., the transformed golden template) from theprevious registration operation as its initial approximation. Then,local optimization techniques may be used to search for a local optimumnear the initial starting approximation. This may be done so that anypotential matches farther away from the feature of interest (e.g., thefemur or tibia in a knee joint) reliably found in an earlier operationmay be eliminated.

For the translation transformation, an exhaustive search may beperformed using a grid 10×10×10 of size 5-millimeter translationvectors. A translation for every vector in the grid may be performed andthe translation providing a maximum local correlation in sample pointsmay be selected as the optimum translation.

For the similarity transformation, a regular step gradient descentoptimizer may be used by one embodiment. A regular step gradient descentoptimizer typically advances transformation parameters in the directionof the gradient and a bipartition scheme may be used to compute the stepsize. The gradient of a function typically points in the direction ofthe greatest rate of change and whose magnitude is equal to the greatestrate of change.

For example, the gradient for a three dimensional space may be given by:

${\nabla{f\left( {x,y,z} \right)}} = {\left( {\frac{\partial f}{\partial x},\frac{\partial f}{\partial y},\frac{\partial f}{\partial z}} \right).}$

That is, the gradient vector may be composed of partial derivatives ofthe metric function over all the parameters defining the transform. Inone embodiment the metric function may be a composition of an outer andN inner functions. The outer function may compute a metric valueaccording to operations 426 and 428 given the vectors {A_(i)} and{B_(i)}. The N inner functions may map N sample points from the fixed(reference) image A_(i) into the target image B_(i) using the transformand evaluate intensities of the target image B_(i) in the mapped points.Each of the inner functions generally depends on the transformparameters as well as on the point in the “from” space to which thetransform is applied. When computing the partial derivatives, the chainrule for computing a derivative of the function composition may be used.

To find a local minimum, parameter steps may be taken in the directionof the negative of the metric gradient (or the approximate gradient)over the transform parameter space at the current point. This generallyoptimizes the metric which typically has a local minimum when featuresof the reference image mapped into corresponding features of the targetimage have minimal misalignment).

The initial center of rotation for the similarity transformation (e.g.,operation 382 of FIG. 17) may be specified as the center of a boundingbox (or minimum sized cuboid with sides parallel to the coordinateplanes) that encloses the feature (e.g., a bone) registered in thetranslation registration (e.g., operation 380 of FIG. 17). Scalingcoefficients of approximately 40-millimeters may be used for the scalingparameters when bringing them together with translation parameters. Itis to be appreciated that the gradient computation generally relies on acustomized metric function in the parameter space, due to the fact thatwith a similarity transformation, the transform parameters do not havethe same dimensionality. The translation parameters have a dimension ofmillimeters, while the parameters for rotational angles and scaling donot have a dimension of millimeters. In one embodiment, a metric ρ maybe defined as

ρ=SQRT(X ² +Y ² +Z ²+(40-millimeter*A1)²+ . . . )

where X is the translation along the x axis, Y is the translation alongthe y axis, Z is the translation along the z axis, A1 is the firstrotation angle, etc. A scaling coefficient of approximately40-millimeters may be used because it is approximately half the size ofthe bone (in the anterior/posterior and medial/lateral directions) ofinterest and results in a point being moved approximately 40-millimeterswhen performing a rotation of one radian angle.

In one embodiment, a maximum move of 1.5-millimeters may be specifiedfor every point, a relaxation factor may be set to 0.98 and a maximum of300 iterations may be performed to determine the parameters of thesimilarity transformation that results in minimal misalignment betweenthe reference image and target MRI scan.

For the affine transformation, a regular step gradient optimizer may beused by one embodiment. Scaling coefficients of approximately40-millimeters may be used for the matrix coefficients variations whenbringing them together with translation parameters. A maximum1.0-millimeter move for every point may be set for each iteration, therelaxation factor may be set to 0.98 and a maximum of 300 iterations maybe performed to determine the parameters of the affine transformationthat results in minimal misalignment.

For the B-spline transformation, a modified regular step gradientdescent optimizer may be used by one embodiment when searching for thebest B-spline deformable transformation. An MRI image gradient may oftenfollow the bone surface in diseased areas (e.g., where the bone contactsurface is severely damaged and/or where osteophytes have grown). Such agradient may cause deformations of the golden template that wouldintroduce large distortions in the segmented bone shape.

In one embodiment, the MRI image gradient may be corrected for suchdeformations by computing a normal to golden boundary vector field whereevery vector points towards the closest point in the golden templateshape found during the affine transformation (e.g., operation 384 ofFIG. 17). This may be done using a distance map (also referred to as adistance transform). A distance map supplies each voxel of the imagewith the distance to the nearest obstacle voxel (e.g., a boundary voxelin a binary image). In one embodiment, the gradient of the signeddistance map of the golden tibia region may be mapped using the affinetransformation found in operation 384 of FIG. 17. In one embodiment, asigned Danielsson distance map image filter algorithm may be used. Then,the MRI image gradient may be projected onto the vector field to obtainthe corrected gradient field. This corrected gradient field is parallelto the normal to golden boundary field and typically defines a very thinsubset of the set of B-spline transformations that may be spanned duringthe optimization.

Additionally, rather than computing one gradient vector for thetransform space and taking a step along it, a separate gradient may becomputed for every spline node. In one embodiment, order three B-splines(with J×K×L control nodes) may be used and J×K×L gradients may becomputed, one for each control point. At every iteration, each of thespline nodes may be moved along its respective gradient. This may allowthe spline curve to be moved in low contrast areas at the same time itis moved in high contrast areas. A relaxation factor of 0.95 may be usedfor each spline node. A maximum move of one-millimeter may be set forevery point during an iteration and a maximum of 20 iterations may beperformed to find the parameters of the B-spline transformation thatprovides minimal misalignment of the golden tibia region mapped into thetarget MRI scan.

Once the position and shape of the feature of interest of the joint hasbeen determined using image registration (operation 370 of FIG. 16), theregistration results may be refined using anchor segmentation and anchorregions (operation 372 of FIG. 16). FIG. 21 depicts a flowchartillustrating one method for refining the registration results usinganchor segmentation and anchor regions. Typically, during thisoperation, one more registration may be done using an artificiallygenerated image for the fixed image 394 of the registration framework390. Use of an artificial image may improve the overall segmentation byregistering known good regions that typically do not change from scan toscan to correct for any errors due to diseased and/or low contrast areasthat otherwise may distort the registration.

Additionally, the artificial image may be used to increase surfacedetection precision of articular surfaces and shaft middle regions. Theimage slices typically have higher resolution in two dimensions (e.g.,0.3-millimeter in the y and z dimensions) and lower resolution in thethird dimension (e.g., 2-millimeters in the x dimension). The articularsurfaces and shaft middle regions typically are well defined in theimage slices due to these surfaces generally being perpendicular to theslices. The surface detection precision may be improved using acombination of 2D and 3D techniques that preserves the in-sliceprecision by only moving points within slices rather than betweenslices. Further, a 3D B-spline transform may be used such that theslices are not deformed independently of one another. Since each slicemay not contain enough information, deforming each slice independentlymay result in the registration finding the wrong features. Instead, theslices as a whole may be deformed such that the registration remainsnear the desired feature. While each slice may be deformed differently,the difference in deformation between slices generally is small suchthat the changes from one slice to the next are gradual.

In one embodiment, the artificial image may comprise a set of dark andlight sample points that may be used by the metric. All dark points inthe artificial image may have the same intensity value (e.g., 100) andall light points in the artificial image may have the same intensityvalue (e.g., 200). It should be appreciated that the correlations aregenerally insensitive to scaling and zero shift. Thus, any intensityvalues may be used as long as the dark intensity value is less than thelight intensity value.

Initially, operation 430 may apply the cumulative registration transform(computed by operation 370 of FIG. 16) to an anchor segmentation meshand its three associated anchor region meshes (e.g., InDark-OutLightmesh, InLight-OutDark mesh and Dark-in-Light mesh) to generate atransformed anchor segmentation mesh and associated transformed anchorregion meshes (transformed InDark-OutLight anchor mesh, transformedInLight-OutDark anchor mesh and transformed Dark-in-Light anchor mesh)that lie in a space identical to the target image space.

Then, operation 432 generates random sample points lying within a thinvolume surrounding the transformed anchor segmentation mesh surface. Inone embodiment, this may be a volume having an outer boundary defined bythe anchor segmentation mesh surface plus 1.5-millimeters and an innerboundary defined by the anchor segmentation mesh surface minus1.5-millimeters, which may be referred to herein as the 1.5-millimeterneighborhood. The random sample points may be generated such that theyare within the image slices of the target scan but not between theslices. For example, the image slices may be transversal to the x-axiswith a spacing of 2-millimeters (at x-axis locations 0.0, 2.0, 4.0, . .. ). When a sample point is selected, its x-coordinate may be one of0.0, 2.0, 4.0, etc. but may not be 1.7, 3.0, or some non-multiple of2.0.

In one embodiment, voxels may be marked in every image slice that belongto the 1.5-millimeter neighborhood as follows. First, the intersectionof the transformed anchor mesh with every image slice may be found. Itshould be appreciated that the intersection of the anchor mesh with animage slice may be a polyline(s). Then, in each image slice, thepolyline segments may be traversed and all pixels that intersect withthe mesh may be marked. Next, a Dilate filter may be applied to themarked pixels of each image slice using a radius of 1.5-millimeters. TheDilate filter typically enlarges the marked region by adding all thepoints that lie within a 1.5-millimeter distance from the originallymarked points.

After operation 432, operation 434 determines if a sample point liesinside the transformed InDark-OutLight mesh surface. If operation 434determines that the sample point lies inside the transformedInDark-OutLight mesh surface, then operation 442 is performed. Ifoperation 434 determines that the sample point does not lie inside thetransformed InDark-OutLight mesh surface, then operation 436 isperformed.

Operation 442 determines if the sample point lies inside the transformedanchor segmentation mesh surface. If operation 442 determines that thesample point lies inside the transformed anchor segmentation meshsurface, then operation 446 is performed. If operation 442 determinesthat the sample point does not lie inside the transformed anchorsegmentation mesh surface, then operation 448 is performed.

Operation 436 determines if the sample point lies inside the transformedInLight-OutDark mesh surface. If operation 436 determines that thesample point lies inside the transformed InLight-OutDark mesh surface,then operation 444 is performed. If operation 436 determines that thesample point does not lie inside the transformed InLight-OutDark meshsurface, then operation 438 is performed.

Operation 444 determines if the sample point lies inside the transformedanchor segmentation mesh surface. If operation 444 determines that thesample point lies inside the transformed anchor segmentation meshsurface, then operation 448 is performed. If operation 444 determinessample point does not lie within the transformed anchor segmentationmesh surface, then operation 446 is performed.

Operation 438 determines if the sample point lies inside the transformedDark-In-Light mesh surface. If operation 438 determines that the samplepoint lies inside the transformed Dark-In-Light mesh surface, thenoperation 440 is performed. If operation 438 determines that the samplepoint does not lie inside the transformed Dark-In-Light mesh surface,then operation 450 is performed.

Operation 440 determines if the sample point is within 0.75-millimeterof the surface of the transformed anchor segmentation mesh. If operation440 determines that the sample point is within 0.75-millimeter of thesurface of the transformed anchor segmentation mesh, then operation 446is performed. If operation 440 determines that the sample point is notwithin 0.75-millimeter of the surface of the anchor segmentation mesh,then operation 450 is performed.

Operation 446 adds the sample point to the artificial image as a darkpoint. Then, operation 450 is performed.

Operation 448 adds the sample point to the artificial image as a lightsample point. Then, operation 450 is performed.

Operation 450 determines if there are more randomly generated samplespoints to be added to the artificial image. If operation 450 determinesthat there are more randomly generated sample points to be added to theartificial image, then operation 434 is performed. If operation 450determines that there are no more randomly generated sample points to beadded to the artificial image, then operation 452 is performed.

FIG. 22 depicts a set of randomly generated light sample points 460 anddark sample points 462 over the target MRI 464. In one embodiment,approximately 8,000 sample points (light and dark) may be generated overthe entire artificial image.

Referring again to FIG. 21, if operation 450 determines that there areno more randomly generated sample points to be added to the artificialimage, operation 452 registers the set of dark and light points to thetarget MRI scan. This operation may perform a registration similar tothe registration operation 196 (depicted in FIG. 17). In thistransformation, a subset of B-spline deformable transformations may beperformed that move points along their respective slices, but nottransversal to their respective slices.

In a B-spline deformable transform, a translation vector for everycontrol point (e.g., in the set of J×K×L control points) may bespecified. To specify a transform that moves any point in 3D space alongthe y and z slice coordinates but not along the x coordinate, arestriction on the choice of translation vectors in the control pointsmay be introduced. In one embodiment, only translation vectors with thex coordinate set equal to zero may be used to move points in the planeof the slice (e.g., the y and z directions) but not transversal to theslice (e.g., the x direction).

The use of anchor region meshes which typically are well pronounced inmost image scans may reduce registration errors due to unhealthy areasand/or areas with minimal contrast differences between the feature to besegmented and surrounding image areas. For example, in the area where ahealthy bone normally has cartilage, a damaged bone may or may not havecartilage. If cartilage is present in this damaged bone region, the boneboundary separates the dark cortical bone from the gray cartilagematter. If cartilage is not present in this area of the damaged bone,there may be white liquid matter next to the dark cortical bone or theremay be another dark cortical bone next to the damage bone area. Thus,the interface from the cortical bone to the outside matter in thisregion of the damaged bone typically varies from MRI scan to MRI scan.In such areas, the interface between the cortical and the innercancellous bone may be used as an anchor region.

The use of a subset of B-Spline deformable transforms may reduce errorsdue to the 2-millimeter spacing between image slices.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves outlining the surface of a feature of interest in eachtarget MRI slice (e.g., operation 376 of FIG. 16). Initially, operation470 intersects the generated 3D mesh model of the feature surface with aslice of the target scan data. The intersection defines a polyline curveof the surface of the feature (e.g., bone) in each slice. Two or morepolyline curves may be generated in a slice when the bone is not verystraightly positioned with respect to the slice direction.

A polyline curve is a piecewise linear approximation to a curved featureshape. Generally, this curve should be easy to manipulate with a set ofcontrol points. The polyline curve may have many segments, making itmore difficult to manipulate the polyline curve (e.g., during operation254 or 260 of FIG. 6). One embodiment may generate one or more Kochaneksplines from the polyline curve. Each spline typically has a smallernumber of control points and typically fits the polyline curve withabout 0.2-millimeter deviation. Generally, a Kochanek spline may havemore control points along the high curvature regions of the polylinecurve and fewer control points along low curvature regions (i.e., wherethe curve tends to be flatter) of the polyline curve.

Once a polyline curve has been generated, operation 472 may compute apolyline parameterization, L_(i), as a function of the polyline'slength. FIG. 24 depicts a polyline curve 481 with n vertices, V₀, V₁, .. . V_(i−1), V_(i) . . . V_(n-1). Note that vertex V₀ follows vertexV_(n-1) to form a closed contour curve. The length of a segmentconnecting vertices Vi−1 and Vi may be denoted by ΔL_(i) such that thelength parameterization, L_(i), of the polyline at vertex V_(i) may beexpressed as:

L _(i) =ΔL ₀ +ΔL ₁ + . . . +ΔL _(i).

Next, operation 474 may compute a polyline parameterization, A_(i), as afunction of the polyline's tangent variation. The absolute value of theangle between a vector connecting vertices V_(i−1) and V_(i) and avector connecting vertices V_(i) and V_(i+1) may be denoted by ΔA_(i)such that the tangent variation parameter A_(i) at vertex V_(i) may beexpressed as:

A _(i) =ΔA ₀ +ΔA ₁ + . . . +ΔA _(i).

Then, operation 476 determines a weighted sum parameterization of thepolyline length and tangent variation parameterizations. In oneembodiment the weighted sum parameterization, W_(i), at vertex V_(i) maybe computed as:

W _(i) =α*L _(i) +β*A _(i)

where α may be set to 0.2 and β may be set to 0.8 in one embodiment.

Then, operation 478 may perform a uniform sampling of the polyline usingthe W parameterization results determined by operation 476. In oneembodiment, a spacing interval of approximately 3.7 of the W parametervalue may be used for positioning K new sample points. First, K may becomputed as follows:

K=ROUND(W _(n)/3.7).

That is, the W parameter value, which is the last computed value W_(n),may be divided by 3.7 and the result rounded to the nearest integer toget the number of new sample points. Then, the spacing of the samplepoints, ΔW may be computed as:

ΔW=W _(n) /K.

Finally, the K new sample points, which are uniformly spaced, may bepositioned at intervals ΔW of the parameter W. The resulting samplepoints may be used as control points for the Kochanek splines to convertthe polyline into a spline. A Kochanek spline generally has a tension, abias and a continuity parameter that may be used to change the behaviorof the tangents. That is, a closed Kochanek spline with K control pointstypically is interpolated with K curve segments. Each segment has astarting point, an ending point, a starting tangent and an endingtangent. Generally, the tension parameter changes the length of thetangent vectors, the bias parameter changes the direction of the tangentvectors and the continuity parameter changes the sharpness in changebetween tangents. In certain embodiments, the tension, bias andcontinuity parameters may be set to zero to generate a Catmull-Romspline.

In one embodiment, operation 478 may perform a linear interpolation ofW_(i) and W_(i+1) to locate a sample point that lies between W_(i) andW_(i+1). The interpolated value of W may be used to determine thecorresponding sample location in the segment connecting vertices V_(i)and V_(i+1).

In certain embodiments, operation 478 may divide the W parameter valueby six to obtain the new number of sample points K. That is,

K=ROUND(W _(n)/6).

Then, a measure of closeness (i.e., how closely the spline follows thepolyline) may be computed as follows. First, the spline is sampled suchthat there are seven sample points in every arc of the spline (i.e., 7*Ksample points). Then, the sum of the squared distances of the samplepoints to the polyline may be computed. Next, the coordinates of the Kcontrol points are varied (i.e., two*K parameters). Then, a localoptimization algorithm is used to find the closest spline. If theclosest spline found during the optimization is not within a certainprecision (e.g., within approximately 0.4-millimeter of the polyline),then the number of control points, K, may be increased by one. The newnumber of control points may be uniformly distributed along the Wparameter, and another optimization performed to find the new closestspline. Generally one to two optimizations provide a spline that followsthe polyline with the desired degree of precision (e.g., withinapproximately 0.2-millimeter).

Finally, operation 480 determines if a spline curve(s) should begenerated for another image slice. If operation 480 determines that aspline curve should be generated for another slice, then operation 472is performed. If operation 480 determines that there are no more imageslices to be processed, the method terminates.

As discussed above, in one embodiment, the output of the segmentationmay be a triangular mesh (e.g., a 3D surface model) of the segmentedbone(s) of a joint (e.g., the femur and tibia of a knee joint). The meshgenerated generally represents a watertight surface that closely followsthe segmentation contour curves of the slices, smoothly interpolatesbetween the segmentation contour curves, and may have a low triangularcount.

In one embodiment, a triangular mesh may be generated as follows. Thesegmentation data may be represented in 3D using (x, y, z) coordinateswith the image slices transversal to the x direction. Thus, thesegmentation contours lie in yz planes with fixed values of x.Initially, an in-slice distance image may be computed for each segmentedslice. The value of each (y, z) pixel in an in-slice distance image isthe distance to the closest point in the contours when the point islocated inside one of the contours and is the inverse (i.e., negative)of the distance to the closest point in the contours when the point isoutside all of the contours.

Then, a marching cubes algorithm may be applied to the in-slice distanceimages to generate the mesh. The marching cubes algorithm is a computeralgorithm for extracting a polygonal mesh of an isosurface (i.e., thecontours) from a three-dimensional scalar field (or voxels). Thealgorithm typically proceeds through the voxels, taking eight neighborvoxels at a time (thus forming an imaginary cube) and determines thepolygon(s) needed to represent the part of the isosurface (i.e.,contour) that passes through the imaginary cube. The individual polygonsare then fused into the desired surface. The generated mesh generallypasses through the zero level of the signed distance function in eachslice such that the mesh lies close to the contours.

It is to be appreciated that the image resolution in the y and zdirections typically determines how well the zero level of the signeddistance function approximates the original contours and may alsodetermine the triangular count in the resulting mesh. In one embodiment,a voxel size of 1.5-millimeters in the y and z directions may be used.This typically yields deviations within 0.1-millimeter of the originalcontours and produces a smooth mesh.

In one embodiment, a smoothing operation may be performed in the xdirection (i.e., transversal to the image slices) to compensate forsurface waviness that may have been introduced when the automaticallygenerated contours were adjusted (e.g., during operation 260 of FIG. 6).Such waviness may occur in regions of an image slice where there isminimal contrast variation and the curve is positioned by thetechnician. Typically a smooth best guess mesh in uncertain areas may bedesired when generating a planning model that may be used to locate theposition of an implant. Alternatively, a smooth overestimation may bedesired in uncertain areas such as in an arthritic model used to createa jig.

In one embodiment, simple smoothing may be used and the amount ofsmoothing (i.e., how much a voxel value may be modified) may becontrolled by two user specified parameters, MaxUp and MaxDown. After anaverage is computed for a voxel, it is clamped using these values tolimit the amount of smoothing. The smoothing operation typically doesnot change the image much in areas where the image contrast is good. Forsmooth best guess averaging in uncertain areas, MaxUp and MaxDown mayeach be set to 1 millimeter. For smooth overestimation averaging inuncertain regions, MaxUp may be set to 2-millimeters and MaxDown may beset to 0-millimeter.

The operation of adjusting segments of the segmentation process will nowbe described with reference to FIG. 25, which depicts a flowchart forone method of adjusting segments (e.g., operation 254 or operation 260of the flowchart depicted in FIG. 6). In one embodiment, thesegmentation data may be manually adjusted by a trained techniciansitting in front of a computer 6 and visually observing theautomatically generated contour curves in the image slices on a computerscreen 9. By interacting with computer controls 11, the trainedtechnician may manually manipulate the contour curves. The trainedtechnician may visually observe all of the contours as a 3D surfacemodel to select an image slice for further examination.

Initially, in operation 482 a slice is selected for verification. In oneembodiment, the slice may be manually selected by a technician.

Next, operation 484 determines if the segmentation contour curve in theselected slice is good. If operation 484 determines that thesegmentation contour curve is good, then operation 494 is performed. Ifoperation 484 determines that the segmentation contour curve is notgood, then operation 486 is performed.

Operation 486 determines if the segmentation contour curve isapproximately correct. If operation 486 determines that the contourcurve is approximately correct, then operation 492 is performed.

In operation 492 incorrect points of the segmentation contour curve maybe repositioned. In one embodiment this may be performed manually by atrained technician. It is to be appreciated that it may be difficult forthe technician to determine where the correct contour curve should belocated in a particular slice. This may be due to missing or unclearbone boundaries and/or areas with little contrast to distinguish imagefeatures. In one embodiment, a compare function may be provided to allowthe technician to visually compare the contour curve in the currentslice with the contour curves in adjacent slices. FIG. 26 depicts animage showing the contour curve 510 (e.g., a spline curve) with controlpoints 512 of the contour curve 510 for the current image slice as wellthe contour curves 514, 516 of the previous and next image slices,respectively, superimposed on the current image slice.

It may be difficult to determine where the correct segmentation contourcurve should be located due to missing or unclear bone boundaries due tothe presence of unhealthy areas, areas with limited contrastdifferences, and/or voxel volume averaging. When visually comparingadjacent slices, the technician may visualize the data in 2D planes (xy,yz, and xz) and in 3D. In one embodiment, the technician may select anarea for examination by positioning a crosshair on a location in anywindow and clicking a mouse button to select that image point. Thecrosshair will be placed at the desired point and may be used toindicate the same location when the data is visualized in each window.

The technician may use the spline control points to manipulate the shapeof the curve. This may be done by using a mouse to click on a controlpoint and dragging it to a desired location. Additionally, thetechnician may add or delete spline curve control points. This may bedone by using a mouse to select two existing control points betweenwhich a control point will be inserted or deleted. Alternatively, thetechnician may use a mouse cursor to point to the location on the curvewhere a control point is to be inserted. In one embodiment, by pressingthe letter I on a keyboard and then positioning the cursor at thedesired location, clicking the left mouse button will insert the controlpoint. A control point may be deleted by pressing the letter D on thekeyboard and then positioning the cursor over the desired control pointto be deleted. The selected control point will change color. Theselected control point will be deleted when the left mouse button isclicked.

Referring again to FIG. 25, if operation 486 determines that the contourcurve is not approximately correct, operation 488 is performed to deletethe curve. Then, operation 490 is performed.

Operation 490 generates a new segmentation contour curve for the imageslice. In one embodiment, a technician may use a spline draw tool toinsert a new spline curve. With the spline draw tool, the technician mayclick on consecutive points in the current slice to indicate where thespline curve should be located and a spline curve is generated thatpasses through all of the indicated points. A right mouse click may beused to connect the first and last points of the new spline curve.Alternatively, the technician may use a paste command to copy the splinecurve(s) from the previous slice into the current slice. The splinecontrol points may then be manipulated to adjust the spline curves tofollow the feature in the current image slice.

In another embodiment, a paste similar command may be used by thetechnician to copy the spline curve from the previous slice into thecurrent slice. Rather than pasting a copy of the spline curve from theprevious slice, the spline curve may be automatically modified to passthrough similar image features present in both slices. This may be doneby registering a region around the spline curve in the previous slicethat is from about 0.7-millimeter outside of the curve to about5.0-millimeter within the curve. Initially, this region is registeredusing an affine transformation. Then, the result of the affine transformmay be used as a starting value for a B-Spline deformabletransformation. The metric used for the transform may be the localcorrelation in sample points metric described previously. Typically,more sample points may be taken closer to the curve and fewer samplepoints taken farther away from the curve. Next, the spline controlpoints may be modified by applying the final transformation found to thespline control points. Additionally, the trained technician may adjustfrom zero to a few control points in areas where the bone boundarychanges a lot from the slice due to the bone being tangent to the sliceor in areas of limited contrast (e.g., where there is an osteophytegrowth). Then, operation 492 is performed.

Operation 494 determines if there are additional slices to be verified.If operation 494 determines that there are additional slices to beverified, operation 482 is performed.

If operation 494 determines that there are no more slices to beverified, then operation 496 is performed. Operation 496 generates a 3Dsurface model of the segmented bone.

Then, operation 498 determines if the 3D surface model is good. In oneembodiment, a technician may manually determine if the 3D surface modelis good. The technician may use a spline 3D visualization tool thatgenerates a slice visualization showing the voxels inside all of thesplines in 3D, as illustrated by the 3D shape 520 depicted in FIG. 27.This spline 3D visualization tool typically may be generated in realtime to provide interactive updates to the technician as the splinecurves are manually edited. Alternatively, a mesh visualization may begenerated in response to a technician command. The mesh visualizationtypically generates a smooth mesh that passes close to all the splinecurves, e.g., mesh 290 depicted in FIG. 9.

If operation 498 determines that the 3D model is not good, thenoperation 500 is performed. Operation 500 selects a slice lying in anarea where the 3D shape is not good.

In one embodiment, a technician may manually select the slice. Then,operation 482 is performed.

If operation 498 determines that the 3D model is good, then the methodterminates.

The 3D surface models of the lower end of the femur and the upper end ofthe tibia of a patient's knee may be used to create arthroplasty jigsand/or implants. For example, the models may be used to create femur andtibia jigs that can be used with a patient's femur and tibia asdisclosed in the various U.S. Patent applications incorporated byreference herein in this Detailed Description and filed by Park and Parket al. Automatic segmentation of image data to generate 3D bone modelsmay reduce the overall time required to perform a reconstructive surgeryto repair a dysfunctional joint and may also provide improved patientoutcomes.

B. Segmentation Using Landmarks of Scanner Modality Image Data toGenerate 3D Surface Model of a Patient's Bone

Now begins a discussion of an alternative embodiment of imagesegmentation. The alternative embodiment includes placing landmarks 777(in FIG. 35A-FIG. 35H) on image contours. The landmarks 777 are thenused to modify a golden bone model (e.g., golden femur or golden tibia),the resulting modified golden bone model being the output ofsegmentation.

Similar to the embodiment of image segmentation discussed above insection b. of this Detailed Discussion, in one version of thealternative embodiment of image segmentation, the 2D images 16 of thepatient's joint 14 are generated via the imaging system 8 (see FIG. 1Aand [block 100] of FIG. 1B). These images 16 are analyzed to identifythe contour lines of the bones and/or cartilage surfaces that are ofsignificance with respect to generating 3D models 22, 36, as discussedabove in section a. of this Detailed Discussion with respect to [blocks110 and 130] of FIGS. 1C and 1D. Specifically, a variety of imagesegmentation processes may occur with respect to the 2D images 16 andthe data associated with such 2D images 16 to identify contour linesthat are then compiled into 3D bone models, such as bone models 22,restored bone models 28, and arthritic models 36.

Algorithms and software are described in this Detailed Discussion forautomatic and semi-automatic image segmentation. In the DetailedDescription, alternative software tools and underlying methods aredescribed, such alternative tools and methods helping a user to quicklygenerate bone models. Because the alternative software requires someuser input such as, for example, initial Landmark positions, finalverification and, in some instances, adjustment, this alternativesegmentation process can be considered a semi-automatic segmentationprocess.

In some cases the alternative embodiment described in section c. of thisDetailed Discussion may significantly reduce the user time spent onsegmentation. In particular, compared to manual segmentation (where theuser draws contour(s) by hand on each applicable slice for eachapplicable bone), the user time may be reduced by approximately fivetimes when a user segments a planning model intended for communicating apreoperative planning model to a surgeon. For that purpose, a user maygenerate 3D bone models with high precision in particular areas and lessprecision in other areas. In some implementations, a user may get highprecision (e.g., 0.5 mm) at well-defined bone contours in MRI imagesoutside the implant regions and less precision (e.g., up to 2 mm) in theregions that will be replaced with implants by spending approximately3-4 minutes in the user interface (“UI”) setting landmarks for thealgorithm. If improved precision is desired, the user may position morelandmarks and thus spend more time in the UI.

In one embodiment, the software tool described in section c. of theDetailed Discussion is called “Segmentation using Landmarks”. This toolmay be implemented inside software application PerForm 1.0. A variety ofprocesses and methods for performing image segmentation using landmarksare disclosed herein.

The imager 8 typically generates a plurality of image slices 16 viarepetitive imaging operations. Depending on whether the imager 8 is aMRI or CT imager, each image slice will be a MRI or CT slice. As shownin FIG. 2A, the image slice may depict the cancellous bone 200, thecortical bone 202 surrounding the cancellous bone, and the articularcartilage lining portions of the cortical bone 202 of an object ofinterest of a joint, e.g., a femur 204 in a patient's knee joint 14. Theimage may further depict the cancellous bone 206, the cortical bone 208of another object of interest in the joint, e.g., a tibia 210 of theknee joint 14. In one embodiment, each image slice 16 may be atwo-millimeter 2D image slice.

One embodiment may segment one or more features of interest (e.g.,bones) present in MRI or CT scans of a patient joint, e.g., knee, hip,elbow, etc. A typical scan of a knee joint may represent approximately a100-millimeter by 150-millimeter by 150-millimeter volume of the jointand may include about 40 to 80 slices taken in sagittal planes. Asagittal plane is an imaginary plane that travels from the top to thebottom of the object (e.g., the human body), dividing it into medial andlateral portions. It is to be appreciated that a large inter-slicespacing may result in voxels (volume elements) with aspect ratios ofabout one to seven between the resolution in the sagittal plane (e.g.,the y z plane) and the resolution along the x axis (i.e., each scanslice lies in the yz plane with a fixed value of x). For example, atwo-millimeter slice that is 150-millimeters by 150-millimeters may becomprised of voxels that are approximately 0.3-millimeter by0.3-millimeter by 2-millimeters (for a 512 by 512 image resolution inthe sagittal plane).

In one embodiment, each slice may be a gray scale image with aresolution of 512 by 512 voxels where the voxel value represents thebrightness (intensity) of the voxel. The intensity may be stored as a16-bit integer resulting in an intensity range from 0 to 65,535, where 0may represent black and 65,535 may represent white. The intensity ofeach voxel typically represents the average intensity of the voxelvolume. Other embodiments may employ scans having higher or lowerresolutions in the sagittal plane, different inter-slice spacing, orimages where the intensity may be represented by a 24 bit vector (e.g.,eight bits each for a red component, green component and bluecomponent). Additionally, other embodiments may store intensity valuesas 8-bit or 32-bit signed or unsigned integers or floating point values.

Typical MRI and CT scan data generally provide images where parts of abone boundary of interest may be well defined while other parts of thebone boundary may be difficult to determine due to voxel volumeaveraging, the presence of osteophyte growth, the presence of tissuehaving similar image intensities in neighboring areas to the object tobe segmented, amongst other things. Such poor definition of parts of thebone boundary in the images may cause fully automated segmentationtechniques to fail. For example, FIG. 2A depicts regions 212 within aslice where an object boundary may not be visible due to neighboringtissue having about the same intensity as the feature of interest.Depicted in FIG. 2B are regions 214 that may be extended into the slicefrom adjacent slices due to a high voxel aspect ratio. Depicted in FIG.2C is a region 216 of the bone boundary 218 that may disappear or loseregularity when the bone boundary 218 is approximately tangent to theslice.

In one embodiment, a user may provide some additional input to theauto-segmentation algorithm, and the algorithm could use the additionaluser input for more accurate and faster segmentation of features ofinterest. For example, the additional user input may be a set of pointson the boundary of the feature of interest. In the context of a kneeprocedure, the points might be on the Femur knee bone boundary or on theTibia knee bone boundary. These can be called landmark points or simplylandmarks 777.

In order for a user to provide particular landmark points, the softwaremay allow loading MRI or CT image data, viewing and scrolling over imageslices, specifying landmark points in the slices and editing them. Thesoftware may also allow visualization of the segmentation results (i.e.,segmentation curves drawn in the image slices). The software may alsogenerate a 3D model from 2D outlining curves in 2D slices.

In one embodiment, PerForm software may be used to provide functionalityfor loading MRI or CT scanned data, visualizing sagittal, coronal andaxial slices and scrolling over them, drawing spline curves in slices,and generating a 3D mesh model passing through a set of spline curves.In one embodiment, a 3D mesh typically is a collection of vertices,edges, and faces that may define the surface of a 3D object. The facesmay consist of triangles, quadrilaterals or other simple convexpolygons. It should be appreciated that any other curve types may beemployed instead of spline curves. For example, polyline curves may beused.

In one embodiment, a tool called “Segmentation using Landmarks” is addedto PerForm software. Such a tool provides a UI for landmarks positioningand editing. The tool also provides a button “Segment”, which invokesthe segmentation algorithm. The algorithm uses 3D image and landmarksand generates spline curves outlining the required bone.

To begin the detailed discussion of the alternative embodiment of imagesegmentation described in this section c. of the Detailed Description,wherein landmarks 777 placed on image contours are used to modify agolden bone model (e.g., golden femur or golden tibia), the resultingmodified golden bone model being segmented, reference is made to FIG.28, which is a diagram depicting types of data employed in the imagesegmentation algorithm that uses landmarks. As shown in FIG. 28, thedata employed in the segmentation algorithm 600 may be characterized asbeing two types of data. The first type of data exists in the systemonce generated and is for use with multiple patients and is notgenerated specifically for the patient for which the current imagesegmentation is being undertaken. This type of data may be called goldenmodel data 602 and is derived similar to as discussed above with respectto FIG. 11, etc. and as generally reiterated below. The golden modeldata 602 may include, for example, one or more golden femur models 603and one or more golden tibia models 604. If the joint being treated issomething other than a knee, for example, the patient's arm, then thegolden model data 602 may include another type of golden bone model, forexample, a golden radius or golden ulna.

The second type of data is specific to the patient for which the currentimage segmentation is being undertaken. This type of data may be calledinput data for segmentation algorithm 606. The input data 606 includes3D image slices data 608, which is 3D image slice data of the patientbone via MRI, CT or another type of medical imaging. The input data 606also includes landmark data 610, which is landmarks 777 positioned onboundaries of the patient bone in the image slices. The input data 606further includes patient bone characteristics 612 such as bone type(e.g., whether the bone is a tibia or femur), bone right or lefthandedness, and whether the segmentation is being done to generate anarthritic model 36 (see FIG. 1D) or a planning or restored bone model 28(see FIG. 1C). As explained below, the golden model data 602 and theinput data 606 are used in the segmentation algorithm 600 to segment the3D image employing landmarks 777.

As shown in FIG. 29, which is a flowchart illustrating the overallprocess for generating a golden femur model 603 of FIG. 28, golden femurscan image slices 616 are obtained in operation 750. For example, asdiscussed above with respect to FIG. 11 above, a representative femur618 that is free of damage and disease may be scanned via medicalimaging, such as, for example, MRI or CT. Where the golden femur model603 is to be employed in generating a bone model 22 (see block 110 ofFIG. 1C) and cartilage geometry is not of interest, the golden femurscan images slices 616 may be of a femur having damaged cartilage aslong as the bone shape is otherwise desirable (e.g., normal) and free ofdeterioration or damage. Where the golden femur model 603 is to beemployed in generating an arthritic model 36 (see block 130 of FIG. 1D)and cartilage geometry is of interest, the golden femur scan imagesslices 616 may be of a femur having both cartilage and bone shape thatare desirable (e.g., normal) and free of deterioration or damage.

The appropriate femur scan may be selected by screening multiple MRIfemur scans to locate an MRI femur scan having a femur that does nothave damaged cancellous and cortical matter (i.e., no damage in femurregions that should be present in this particular model), which has goodMRI image quality, and which has a relatively average shape, e.g., theshaft width relative to the largest part is not out of proportion (whichmay be estimated by eye-balling the images). This femur scan data,referred to herein as a golden femur scan, may be used to create agolden femur template.

It is to be appreciated that several MRI scans of a femur (or other boneof interest) may be selected, a template generated for each scan,statistics gathered on the success rate when using each template tosegment target MRI scans, and selecting the one with the highest successrate as the golden femur template.

In other embodiments, a catalog of golden models may be generated forany given feature, with distinct variants of the feature depending onvarious patient attributes, such as (but not limited to) weight, height,race, gender, age, and diagnosed disease condition. The appropriategolden mesh would then be selected for each feature based on a givenpatient's characteristics.

In operation 752 and as indicated in FIG. 30, each of the image slices616 of the representative femur 618 are segmented with a contour curveor spline 620 having control points 622 and in a manner similar to thatdiscussed above with respect to FIG. 12A, etc. For example and as shownin FIG. 30, where the golden model is to be used in the generation of abone model 22 (see block 110 of FIG. 1C) and cartilage geometry is notof interest, each segmentation region includes cancellous matter andcortical matter of the femur in a manner similar to that discussed abovewith respect to the cancellous matter 322 and cortical matter 324 of thetibia depicted in FIG. 12A, etc. Thus, as shown in FIG. 30, the contourcurve 620 excludes any cartilage matter in outlining a golden femurregion.

On the other hand, where the golden model is to be used in thegeneration of an arthritic model 36 (see block 130 of FIG. 1D) andcartilage geometry is of interest, each segmentation region the contourcurve would include cartilage matter in outlining a golden femur region.

If the golden femur scan does not contain a sufficiently long shaft ofthe femur bone (e.g., it may be desired to segment a femur in a targetMRI that may have a longer shaft), then the image segmentation can beextrapolated beyond the image to approximate a normal bone shape. Thiscan be done because the femoral shaft is quite straight and, generally,all that is needed is to continue the straight lines beyond the MRIimage, as can be understood from the extension of the contour line 620proximal of the proximal edge of the femur image 616 of FIG. 30.

In operation 754 and as illustrated in FIG. 31A, the contour curves orsplines 620 are compiled and smoothed into a golden femur mesh 624 asdiscussed above with respect to FIG. 13A, etc. As indicated in FIG. 30,in one embodiment, the segmentation curve 620 is a closed curve. Thus,the resulting golden femur mesh 624 is a closed mesh as depicted in FIG.31A.

In operation 756 and as shown in FIG. 31B, the golden femur mesh 624 isconverted into an open golden femur mesh 626, wherein the proximalportion of the golden femur mesh 624 is removed to create the opensurface model called the open golden femur mesh 626. In other words, inoperation 756 the artificial part of the femur mesh 626 is cut off,namely the proximally extending shaft portion that results from theproximal extrapolated extension of the contour line 620, so as to obtainthe open golden femur mesh 626 of FIG. 31B.

In operation 758 and as indicated in FIG. 31C, regions 628, 629 of adifferent precision are generated for the golden femur mesh 626. Forexample, when segmenting the image slices 16 for the purpose ofgenerating a golden femur mesh 626 that is used to create a 3D computergenerated bone model used to show the preoperative planning (“POP”)images to a surgeon, it is desirable that the bone geometry of the mesh626 be generated with a relatively high degree of accuracy in certainregions 628 of the mesh 626 such that the resulting 3D computergenerated bone model allows the physician to verify the POP with adesired degree of accuracy, while other regions 629 of the mesh 626 maynot be generated to such a high degree of accuracy. For example, such adegree of accuracy in the certain regions 628 of the mesh 626 can beachieved via relatively precise image segmentation. The certain regions628 of the mesh 626 having the relatively high degree of accuracy couldinclude, among others, the lower shaft area, as depicted in FIG. 31C. Inone embodiment, the relatively high accuracy of the certain regions 628of the mesh 626 should allow the physician to verify the POP within 0.5mm accuracy.

As can be understood from FIG. 31C, in one embodiment, the highprecision region(s) 628 of the mesh 626 represent a portion of thedistal anterior femoral shaft that would be contacted by the anteriorflange of a candidate femoral implant. The rest of the mesh 626 may formthe region 629 that has an accuracy that is not as precise as the highprecision region 628. Such a lower precision region 629 of the mesh 626may include the entire distal femur excluding the distal anterior regionof the shaft included within the high precision region 628. Where thegolden femur mesh 626 is employed to form other 3D computer generatedbone models, such as, for example, the bone model 22 or arthritic model36, the mesh 626 may have a different number of high precision regions628 (e.g., none, one, two, three, or more such regions 628). Also, suchregions 628 may have precisions that are greater or less than statedabove. Finally, such regions 628 may correspond to different regions ofthe bone, encompass generally the entirety of the mesh surface, orinclude other regions in addition to the region 628 depicted in FIG.31C.

While the preceding discussion regarding the open golden bone mesh isgiven in the context of the open golden bone mesh being an open goldenfemur mesh 626, as can be understood from FIG. 32A-B, the open goldenbone mesh may be an open golden tibia mesh 630 having regions 632, 633of a different precision, all of which are generated in a manner similarto that discussed with respect to FIGS. 28-31C above.

For example, as can be understood from FIGS. 32A-32B, in one embodiment,the high precision region(s) 632 of the open golden tibia mesh 630represent a portion of the proximal anterior tibial shaft immediatelydistal the tibial plateau and running medial to lateral generallyproximal the tibial tuberosity. Another high precision region 632 mayoccupy a space similar in location and size, except on the posterior ofthe tibial shaft. The rest of the mesh 630 may form the region 633 thathas an accuracy that is not as precise as the high precision region 632.Such a lower precision region 633 of the mesh 630 may include the entireproximal tibia excluding the regions of the shaft included within thehigh precision regions 632. Where the golden tibia mesh 630 is employedto form other 3D computer generated bone models, such as, for example,the bone model 22 or arthritic model 36, the mesh 630 may have adifferent number of high precision regions 632 (e.g., none, one, two,three, or more such regions 632). Also, such regions 630 may haveprecisions that are greater or less than stated above. Finally, suchregions 630 may correspond to different regions of the bone, encompassgenerally the entirety of the mesh surface, or include other regions inaddition to the regions 632 depicted in FIGS. 32A-32B.

For a discussion of an alternative embodiment of operations 250-254 ofFIG. 6, reference is first made to FIG. 33, which is a flowchartillustrating the alternative embodiment of segmenting a target bone. Inthis example, the target bone is a femur 204, but may be a tibia 210 orany other type of bone.

As indicated in FIG. 33, operation 250 obtains or, more specifically,loads the scan data (e.g., scan images 16) generated by imager 8 of thepatient's joint 14 to be analyzed. In operation 251 the landmarks arepositioned in the scan images. In other words, as can be understood fromFIG. 34, which is a flowchart illustrating the steps of operation 251,operation 251 begins with operation 251 a, wherein the images 16 arescrolled through (e.g., medial to lateral or lateral to medial) to themost medial or lateral image slice were the femur bone 204 firstappears, as shown in FIG. 35A, which, in this example, is a most lateralsagittal MRI image slice 16 where the femur bone 204 or, morespecifically, the lateral epicondyle 776 first appears. Since the slice16 of FIG. 35A is the most lateral image where bone has begun to appear,the fibula 775 can be seen adjacent the tibia 210 in such instanceswhere the image slice is positioned so as to show both the femur 204 andthe tibia 210. In operation 251 b, two or more landmarks 777 arepositioned on the outer rim of the black cortical bone 208 of the imageslice 16 depicted in FIG. 35A. As is the case with all of the imagesdepicted in FIGS. 35A-35H, in one embodiment, the landmarks are placedvia an operator sitting at a work station. In one embodiment, theoperator or user is able to add landmarks by simply clicking onto theslice image, the landmark (point) being created at the exact coordinateswhere the click has occurred. The operator is able to move existinglandmarks within the slice by selecting them and moving them with themouse, a keyboard, a pen-and-tablet system, or similar. The user is ableto delete existing landmarks by selecting them and indicating to thesoftware that they should be deleted.

In another embodiment, a touch-screen surface may be used to provideinput and display for interactive editing of landmarks and segmentationcurves. Specialized gestures may be adopted for various editingoperations.

In another embodiment, a spatial input device may be used formanipulation of landmarks, segmentation curves, and other operationsinvolving POP and jig design activities.

In operation 251 c, the image slices 16 are scrolled lateral to medialthrough approximately three slices 16 further to a new image slice 16and, at operation 251 d, it is determined if the femur bone 204 is stillvisible in the new image slice 16, which is depicted in FIG. 35B. If so,then operation 251 e adds landmarks 777 to the new image slice 16 asindicated in FIG. 35B. Specifically, as indicated in FIG. 35B, this newimage slice 16 may show the femur lateral condyle 778 and be the firstimage slice having a clear boundary 779 of the femur lateral condyle. Ascan be seen in FIG. 35B, the fibula 775 and tibia 210 are also morefully shown. Landmarks 777 are set on the clear boundary 779 of theouter rim of the dark cortical bone of the femur lateral condyle, and anadditional landmark 777 is set on the opposite side 780 on the rim ofthe black cortical bone 208. As is the case with the placement oflandmarks 777 in any of the images 16, more or fewer landmarks 777 maybe placed along the rim of the black cortical bone depicted in the image16, including landmarks being placed on the rim of the black corticalcone of the entirety of the distal femur, including the distal femurcondyle and distal femur shaft.

Operations 251 c through 251 e are repeated to set landmarks 777 at thebone contour boundaries of approximately every third image slice 16moving lateral to medial until eventually at operation 251 d it isdetermined that bone no longer appears in the present image slice. Thus,as operation 251 of FIG. 33 loops through operations 251 c-251 e of FIG.34, landmarks 777 are set at the bone contour boundaries in each of thesagittal image slices 16 depicted in FIGS. 35C-35H, which are,respectively, approximately every third sagittal image slice 16 tabbinglateral to medial through all the sagittal image slices 16 loaded inoperation 250 of FIG. 33. Thus, as shown in FIG. 35C, which represents asagittal image slice 16 approximately three slices more medial than theimage slice 16 of FIG. 35B, the femur lateral condyle 778 has a clearbone contour boundary 779, and landmarks 777 are set along the boundary779 on the rim of the dark cortical bone 208. A landmark 777 is also seton the top region 780 of the cortical bone boundary 779 where the bonecontour boundary is less clear, the landmark being positioned on the rimof the dark cortical bone 208.

As illustrated in FIG. 35D, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35C, the femur shaft 781 has now appeared in an image slice 16 andboth the femur shaft 781 and femur lateral condyle 778 have clear bonecontour boundaries 779. Landmarks 777 are set along the bone contourboundaries 779 on the rim of the dark cortical bone 208.

As shown in FIG. 35E, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35D, the femur lateral condyle 778 is starting to disappear, andpart of its cortical bone contour boundary 779 is not clear. Landmarks777 are only set outside the dark cortical bone 208 in the regions wherethe contour boundary 779 is clear.

As illustrated in FIG. 35F, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35E, the bone contour boundary 779 has become less clear as thefemur lateral condyle 778 has decreased in size as compared to the femurlateral condyle 778 of slice 16 in FIG. 35E. The slice 16 of FIG. 30F isjust lateral of the trochlear groove 782 between the femur lateralcondyle 778 and femur medial condyle 783. The bone contour boundary 779is clear in the anterior region of the femur lateral condyle 778 and twolandmarks 777 are placed there. Additional landmarks 777 are set alongthe bone contour boundaries 779 on the rim of the dark cortical bone208.

As indicated in FIG. 35G, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35F, landmarks 777 are set along the bone contour boundaries 779 onthe rim of the dark cortical bone 208. The slice 16 of FIG. 35G is inthe trochlear groove 782 between the femur lateral condyle 778 and femurmedial condyle 783. The intercondylar eminence 784 of the tibia 210 canbe seen in the slice 16 of FIG. 35G.

As indicated in FIG. 35H, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35G, the femur shaft 781 has begun to disappear and the femurmedial condyle 783 has begun to appear as the slice of FIG. 35H ismedial of the trochlear groove 782 depicted in the slice of FIG. 35G.The bone contour boundary 779 is clear in the anterior region of thefemur medial condyle 783 and two landmarks 777 are placed there.Additional landmarks 777 are set along the bone contour boundaries 779on the rim of the dark cortical bone 208.

As stated above, operations 251 c through 251 e continue to be repeatedas the slices 16 continue to be tabbed through lateral to medial to setlandmarks 777 at the bone contour boundaries of approximately everythird image slice 16 until eventually at operation 251 d it isdetermined that bone no longer appears in the present image slice.Operation 251 f then scrolls medial to lateral through the image slices16 until arriving at the image slice 16 where the most medial portion ofthe femur is depicted. Operation 251 g then sets two or more landmarks777 around the bone (e.g., the medial epicondyle) in a manner similar tothat depicted in FIG. 35A with respect to the lateral epicondyle 776.This is the end of operation 251 and, as can be understood from FIG. 33,operation 252 begins by pressing the “segment” button (operation 252 a),which causes segmentation lines to be generated for each slice 16 withlandmarks 777 (operation 252 b) in a manner similar to that illustratedand discussed above with respect to FIGS. 7A-7K or as now will bediscussed below beginning with FIG. 36.

When positioning landmarks, a user needs to distribute them over thecortical bone outer surface, favoring areas where the cortical boneboundary is sharp and is more orthogonal to the slice plane,particularly favoring certain “important” areas of the bone surface(where importance is dictated by eventual contact between bone andimplant or by other requirements from POP procedure.) The user shouldonly sparsely mark up the remaining parts of the bone, particularlywhere there is a lot of volume averaging (and/or the bone surface ismore parallel to slice plane.) While the image slices depicted in FIGS.35A-35H are MRI generated image slices, in other embodiments the imagingslices may be via other medical imaging methods, such as, for example,CT.

In one embodiment, the landmark-driven segmentation algorithm describedbelow is deliberately sensitive to the number of landmarks (points)placed at a given area of the bone. So for instance, if the user desiresthe auto-generated bone mesh to very accurately pass through particularspots on the slice, the user can place more than one landmark on thatsame spot or very near that spot. When there is a high concentration oflandmarks in a small area of the bone, the auto-generated mesh will bebiased to more accurately model that area. The software indicates to theuser, making it visible at a glance whenever more than one landmark islocated within the same small area of the image.

In one embodiment, instead of putting landmarks in every three slices, auser may position landmarks in every slice but use three times fewerlandmarks in each slice. The result of the segmentation usually variesvery little depending on how a user distributes landmarks around thebone surface as long as the entire surface is covered.

While much of the following discussion takes place in reference to thesegmentation of a femur (operation 252 of FIG. 6), the conceptsdiscussed herein are readily applicable to the segmentation of a tibia(operation 258 of FIG. 6). Additionally, the concepts discussed hereinare readily applicable to both the left or right knee. Different goldentemplate data may be used to segment the left tibia, right tibia, leftfemur or right femur for bone models 22 or planning models 28.Additionally, other embodiments may segment other models and or joints,including but not limited to, arthritic models 36, hip joints, elbowjoints, etc. by using an appropriate golden template of the feature ofinterest to be segmented.

As shown in FIG. 36, which is a flowchart illustrating the process ofsegmenting the target images 16 that were provided with landmarks 777 inoperation 251, the full or entire golden femur mesh 626, including itsregions 628, 629 in FIG. 31C, is deformed in operation 770 to matchlandmarks 777 and appropriate features, such as, for example, the outeredges of dark cortical bone, in the target scan images 16.

As discussed below with respect to FIG. 37, a method is provided formapping the golden femur mesh into the target scan using registrationtechniques. Registration may be thought of as an optimization problemwith a goal of finding a spatial mapping that aligns a fixed object witha target object. Generally, several registration operations may beperformed, first starting with a low-dimensional transformation group tofind a rough approximation of the actual femur location and shape in thetarget image. This may be done to reduce the chance of finding wrongfeatures instead of the femur of interest. For example, if a free-formdeformation registration was initially used to register the golden femurmesh to the target scan data, the template might be registered to thewrong feature, e.g., to a tibia rather than the femur of interest. Acoarse registration may also be performed in less time than a fineregistration, thereby reducing the overall time required to perform theregistration. Once the femur has been approximately located using acoarse registration, finer registration operations may be performed tomore accurately determine the femur location and shape. By using thefemur approximation determined by the prior registration operation asthe initial approximation of the femur in the next registrationoperation, the next registration operation may find a solution in lesstime. It is to be understood that similar considerations apply tosegmentation of other entities (and not just the femur.)

In one embodiment, each registration operation may employ a registrationframework. The registration framework may be based on three generalblocks. The first block defines a transformation model (or a class oftransforms) T(X), which may be applied to coordinates of a fixed (orreference) object (e.g., a golden femur template) to locate theircorresponding coordinates in a target image space (e.g., an MRI scan).The second block defines a metric, which quantifies the degree ofcorrespondence or similarity between features of a fixed (or reference)object and the target object (that is landmarks and appropriate targetimage features) achieved by a given transformation. It should be notedthat instead of a metric that defines the degree of correspondence, anopposite to it function is defined, which is call the defect function.The third block defines an optimization algorithm (optimizer), whichtries to maximize the reference and the target objects similarity (orminimize the opposite defect function) by changing the parameters of thetransformation model. Thus, as discussed below in detail with referenceto FIG. 37, in every registration operation 770 a-770 c and 770 e thereis a need to specify three blocks: (1) class of transforms; (2) metric(or defect) function; and (3) optimization algorithm. In one embodiment,the same third block may be used in all four registration steps. Forinstance, a gradient descent optimizer or conjugate gradient descendoptimizer may be used. Alternatively, any other appropriate optimizationalgorithm, such as Monte Carlo, simulated annealing, genetic algorithms,neural networks, and so on, may be used.

As shown in FIG. 37, which is a flowchart illustrating the process ofoperation 770 of FIG. 36, in operation 770 a translation transforms areused to register the full or entire open golden femur mesh 626 to thelandmarks 777. More specifically, in operation 770 a, the open goldenfemur mesh 626 may be approximately registered to landmarks 777 using acoarse registration transformation. In one embodiment, this may be doneby finding appropriate translation transform parameters that minimizetranslation misalignment with landmarks of the reference open goldenfemur mesh mapped onto the target femur of the target image, wherelandmarks 777 are positioned. This coarse registration operationtypically determines an approximate femur position in the MRI scan.During this operation, the reference open golden femur mesh 626 may beoverlapped with the target femur of the target image using a translationtransformation to minimize translational misalignment of the femurs. Atranslation transform, translates (or shifts) all the points in 3D spaceby the same 3D vector. That is, the reference femur may be mapped intothe target image space by shifting the reference open golden femur meshalong one or more axes in the target image space to minimizemisalignment. During this operation the reference object is not rotated,scaled or deformed. In one embodiment, three parameters for thetranslation transformation may be generated: one parameter for eachdimension that specifies the translation for that dimension. In oneembodiment, the final parameters of the translation transform minimizingthe misalignment of the mapped reference femur image coordinates intothe target image space may be found using a gradient descent optimizer.In other embodiments, other types of optimizers may be utilized, such asfor instance an Iterative Closest Point (ICP) algorithm.

Optimization of mesh alignment with respect to landmarks is based onminimizing a cost function D, which in one embodiment can be the sum,across all landmarks, of the squared distance from each landmark point777 to the transformed open golden mesh. The same cost function may beused for steps 770 a-770 c. Methods for computing this cost function andits gradient are covered in more detail later in this disclosure.

After an optimal transform has been found, it is applied to all thegolden femur data (i.e., the closed golden femur mesh 624, open goldenfemur mesh 626, and golden femur mesh regions 628, 629. The nextoperation (i.e., operation 770 b of FIG. 37, which is discussedimmediately below) is then started with transformed golden femur data.As can be understood from the following discussion, after everyconsecutive operation 770 a, 770 b, 770 c and 770 e of FIG. 37, thetransform found during the registration step is applied to all thegolden femur data. As a result, after each operation the golden femurdata is successively made more closely aligned with the femur in thetarget image.

In operation 770 b of FIG. 37 similarity transforms are used to registerthe full or entire open golden femur mesh 626 to the landmarks 777.Specifically, operation 770 b further refines the object's registrationdetermined by operation 770 a. This may be done by approximatelyregistering the open golden femur mesh 626 to landmarks 777 using asimilarity transformation. In one embodiment, a similaritytransformation may be performed in 3D space. The reference open goldenfemur mesh may be rotated in 3D, translated in 3D and homogeneouslyscaled to map its coordinates into the target MRI scan data to minimizemisalignment between the open golden femur mesh and the landmarks in thetarget MRI scan. In some embodiments, a center of rotation may bespecified so that both the rotation and scaling operations are performedwith respect to the specified center of rotation. In one embodiment, a3D similarity transformation, specified by seven parameters, may beused. One parameter specifies the scaling factor, three parametersspecify a versor that represents the 3D rotation, and three parametersspecify a vector that represents the 3D translation in each dimension. Aversor is a unit quaternion that provides a convenient mathematicalnotation for representing rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference open golden femur mesh onto the target MRI scan that is notfar from the registration of the reference open golden femur mesh ontothe target MRI scan found in previous operation 770 a and used as theinitial starting approximation. For instance, gradient descent,conjugate gradient descent, or ICP optimization may be used. After thebest transform is found for operation 770 b of FIG. 37, the transform isapplied to the golden femur data in a manner similar to that ofoperation 770 a.

In operation 770 c of FIG. 37 affine transforms are used to register thefull or entire open golden femur mesh 626 to the landmarks 777.Specifically, operation 770 c further refines the image registrationdetermined by operation 770 b. In one embodiment, an affinetransformation may be used to register the open golden femur mesh 626 tolandmarks 777 in the target MRI scan data. In one embodiment, theapproximate femur registration found during operation 770 b may be usedas the initial starting approximation for the affine transformation ofoperation 770 c.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe open golden femur mesh with landmarks may be found using again localminimization techniques, such as gradient descent or conjugate gradientdescent optimization.

After the best transform is found for operation 770 c of FIG. 37, thetransform is applied to the golden femur data. The transformed goldenfemur data from operation 770 c is then employed in the preparatory stepof detecting appropriate image edges, namely, operation 770 d, which isdiscussed below. Those edges will be later used in operation 770 e ofFIG. 37, as discussed below. The transformed golden femur data fromoperation 770 c is used as reference data similar to the previousoperations.

A discussion of image edges is now provided before discussing thedetails of operation 770 d of FIG. 37. Image edges consist of thosepoints in the image where the image contrast significantly changesbetween neighbor pixels (or voxels) and this contrast change isconsistent along several neighboring points distributed over a smoothcurve. For example, points that lie between the light cancellous bonepixels and dark cortical bone pixels form an image edge. Similarly, thepoints that lie between the dark cortical bone pixels and the grayishcartilage pixels form an image edge. Yet a configuration involving aone-pixel black spot and the surrounding light pixels does not form animage edge because the light points represent a curve with too muchcurvature, whereas the dark point represents a curve that is toodiscontinuous (spanning only a single voxel.) Usually there is an imageedge that separates one type of the body tissue from a neighboringdifferent type of body tissue.

The purpose of segmenting an image is to be able to separate in theimage certain body tissues from the surrounding tissues. Ideally, thesegmentation boundaries (or curves) should lie mostly in the imageedges. A general MRI or CT image contains lots of edges separatingvarious body tissues from the neighboring tissues. Yet when segmenting,there is only interest in certain tissues and thus particular edgesonly. Operation 770 d is intended to find those edges that are ofinterest for segmenting a particular body object.

In particular in case of the segmentation of any of the versions of thefemur planning model 22, 28 (shown in blocks 110 and 115, respectively,of FIG. 1C), operation 770 d of FIG. 37 will find the edges thatseparate the cortical femur bone from the outside knee tissues (i.e.,the tendons, ligaments, cartilage, fluid, etc.). In some embodiments,operation 770 d will not find the edges that separate the femurcancellous bone from the femur cortical bone. In other embodiments,operation 770 d will find the edges that separate the femur cancellousbone form the cortical bone.

Operation 770 d may also find some edges that are of no interest to thefemur planning segmentation. Most of those edges of no interest will lieat significant distance from the femur boundary surface and, as aresult, the edges of no interest will not influence the next operationin the algorithm, namely, operation 770 e of FIG. 37.

In some cases, some of the edges of no interest might happen to be veryclose to the edges of interest. Such nearby edges of no interest arelikely to be the edges separating the cartilage tissue from the othertissues outside the bone. Such edges might adversely influence the nextoperation in the algorithm, namely, operation 770 e of FIG. 37, and leadto inaccurate segmentation. In some embodiments, this inaccuracy can beremedied by the user providing extra landmarks 777 in the area that islikely to cause such inaccuracies or manually fixing the spline curvesduring the verification and adjustment operations.

The result of the operation 770 e of FIG. 37 will be a 3D image of thesame size as the target scan data. The resulting 3D image can be calledan edges image. The voxels in the edges image correspondent to strongedges will have highest intensities, the non-edge voxels will have lowintensities, and the voxels correspondent to weak edges will haveintermediate intensities. Discussion of the operation 770 d of FIG. 37is now provided.

In operation 770 d of FIG. 37 appropriate edges of the target images aredetected near the transformed open golden femur mesh 626. For example,as indicated in FIG. 38A, which is a flowchart illustrating the processof operation 770 d of FIG. 37, in operation 770 d 1 the signed distanceimage is computed for the transformed golden femur mesh 626. A signeddistance map is a distance map of a region in 2D (or 3D) and is afunction in 2D (or 3D). The signed distance value for a point equals thedistance from the point to the boundary of a region. A signed distancevalue can have a positive or negative value. For example, when a pointis inside the region, the signed distance value of the point is thedistance from the point to the boundary of the region in the form of anegative value. When a point is outside the region, the signed distancevalue of the point is the distance from the point to the boundary of theregion in the form of a positive value. If the signed distance mapfunction is computed in a regular grid of points in 2D (or 3D)correspondent to image pixels (or voxels) and stored as a 2D (or 3D)image representation, the result can be said to be a 2D (or 3D) signeddistance image.

Thus, from the preceding discussion, it can be understood that thesigned distance for a watertight surface is a function that has absolutevalues equal to the regular (Euclidean) distance, but the values alsohave a sign. The sign is negative for the points inside the surface, andthe sign is positive for the points outside the surface. The open goldenfemur mesh 626 transformed in operations 770 a-770 c of FIG. 37 is usedin operation 770 d 1 of FIG. 38A. By the time of operation 770 d 1, theopen golden femur mesh 626 may quite closely match the landmarks 777positioned in the target image and, as a result, the open golden femurmesh 626 also matches quite closely the target femur bone in the targetimage. Since the golden femur mesh 626 is a watertight mesh, the maskimage marking may be computed as “1” for all voxels that lie inside theopen golden femur mesh 626 and as “0” for all the voxels that lieoutside the mesh. The Signed Danielsson Distance Map Image Filter fromthe ITK library can then be used to compute the signed distance to themask boundary, which is approximately the same as the signed distance tothe mesh. It may be desired to have greater accuracy close to the mesh.If so, then for the voxels where the absolute value of the signeddistance is small, the distance to the mesh may be recomputed by findingthe closest points via a more exact method, as detailed later in thisspecification.

In operation 770 d 2 the gradient of the signed distance image iscomputed. As can be understood from FIG. 38B, the gradient of the signeddistance image contains a vector 1000 in every voxel. The vector 1000represents the gradient of the signed distance image at the particularpoint of the voxel. Because the signed distance image represents thesigned distance to the transformed open golden femur mesh 626, whichfollows closely the boundary of the femur bone in the target image, thegradient image has gradient vectors nearly orthogonal to the boundary ofthe target femur in the voxels close to the boundary.

The contour line 626 in FIG. 38B represents the approximate segmentationmesh surface found in the previous registration step of operation 770 cof FIG. 37. The vectors 1000 show the gradient of the signed distancefor the contour 626. The starting end of the vector 1000 is the point orvoxel where the vector 1000 is computed. The gradient of a signeddistance has a vector direction in every point or voxel toward theclosest point in the contour 626. Vectors 1000 are oriented from insideto outside the contour 626. Each vector 1000 has a unit length.

In operation 770 d 3 the gradient of the target image is computed. Ascan be understood from FIG. 38C, which is an enlarged view of the areain FIG. 38B enclosed by the square 1002, the gradient of the targetimage has gradient vectors 1004 orthogonal to the edges 1006, 1008 inthe target image, and the length of those vectors 1004 is larger forstronger edges and smaller for weaker edges. Such vectors 1004 arealways oriented from the darker image region to the lighter image regionor, in other words, from darker pixels towards brighter pixels. Thevectors 1004 are longer where the contrast is higher. For purposes ofillustration in FIG. 38C, the vectors 1004 illustrated are only longvectors corresponding to high contrast pixels associated with strongedges. The gradient vectors 1004 can be used to identify the outercortical bone boundary 1006 and other edges 1008, 1010 that are not ofinterest for the analysis.

Finally, operation 770 d of FIG. 37 is completed via operation 770 d 4of FIG. 38A, wherein the edges image is computed by correcting thegradient of the target image with the gradient of the signed distanceimage. As can be understood from FIG. 38D, the edges image is computedby combining the results from operations 770 d 2 and 770 d 3. Dependingon the type of 3D computer generated bone model being generated from thesegmented images, different boundary edges may be of relevance. Forexample, if the images are being segmented to generate a bone model 22,the boundary edges that are of interest contain dark cortical voxelsinside and lighter cartilage or other voxels outside. As a result, thevoxels that are of interest are those voxels that have similarlyoriented gradients 1000, 1004 computed in operations 770 d 2 and 770 d 3as shown in FIGS. 38B and 38C, respectively. In every voxel the vector1004 from operation 770 d 3 is projected onto the vector 1000 fromoperation 770 d 2. When the projection of image gradient vector onto asigned distance gradient vector points in the same direction as thesigned distance vector, its magnitude is taken as the voxel value forthe resulting edges image. When it points in the opposite direction (orhas no magnitude at all), “0” is taken as the voxel value for theresulting edges image.

The resulting edges image has high values in the target femur corticalbone outer boundary 1006. However, the edges image does not have manyother high values close to the transformed open golden femur mesh withone exception, namely, the voxels on the boundary between the targetfemur cartilage and the outsight bright voxels (for example fluidvoxels) might have high values.

As can be understood from FIG. 38D, the gradient of the signed distancevectors 1000 are uniformly oriented orthogonal to the bone surface andgo from inside to outside of the bone. The image gradient vectors 1004are oriented differently near different image details. For the points inthe bone outer boundary 1006, the vectors 1004 are almost parallel tothe vectors 1000. For two of the other boundaries 1008, the vectors1000, 1004 are generally oppositely oriented. Finally, for the lastboundary 1010, the vectors 1000, 1004 are quite differently orientedfrom each other. As a result of the combination of the vectors 1000,1004 and an analysis of their directional relationships to each other,the edges image will be for FIG. 38D as follows. For points associatedwith the bone contour line 1006, the edges image will reflect the lengthof the image gradient vector. For points associated with contour lines1008, the edges image will be zero. For points associated with thecontour line 1010, the edges image values will be smaller than thelength of the image gradient vector associated with the bone contourline 1006. Thus, the edges image will tend to have the largest valuesfor the points of the bone contour line 1006.

The high values correspondent to a cartilage/fluid boundary mightnegatively impact operation 770 e of the registration in FIG. 37.Consequently, it may be desirable to suppress those values. This can bedone in the beginning of operation 770 d 3 of FIG. 38A. Specifically, awindowing filter may be applied to the whole target image. A window [w0,w1] may be used, where w0 will be the minimum value in the image, and w1will be approximately the value correspondent to the cartilageintensity. The filter will replace the high intensity values in theimage with w1 value, and thus the boundary between the cartilage and thelighter matters will disappear. For the type of MRI images that may beused, the w1 value correspondent to the median of all the values in theimage works quite well. Although such a filter may not always suppressthe cartilage boundary entirely, it makes cartilage outer boundary verymuch weaker in the image and, as a result, the cartilage has less of animpact in operation 770 e of FIG. 37.

In one embodiment, a more sophisticated method for suppressing thecartilage boundary may be employed. The cartilage intensity values maybe estimated by comparing the voxel values near landmarks 777 along thesigned distance gradient direction. The values before a landmarkcorrespond to the cortical bone intensities, while the values after thelandmark correspond to the cartilage intensity. Thus for every landmark,a value may be found that represents an “Out of cortical bone”intensity. Such values may be interpolated into the whole image and thiswindowing function may be applied rather than the constant windowingvalue w1.

It should be appreciated that a lesser resolution than the target imageresolution may be used in all the images participating in the edgesimage computation. For example, an in-slice voxel size of 1 mm may beused rather than ˜0.3 mm in the target image. Using coarser resolutionin effect smoothes out the data, allowing a more stable edgescomputation. It also significantly speeds up the computation. In case ofvery noisy target images, an additional smoothing step may be applied.

Operation 770 of FIG. 36 is completed via operation 770 e of FIG. 37,wherein the full or entire golden femur mesh 626, including its regions628, 629, are simultaneously registered to landmarks 777 and image edgesrespectively using B-spline deformable transforms. Specifically,operation 770 e of FIG. 37 further refines the image registration of theboundary golden femur region. In one embodiment, a spline transformationmay be used to register the open golden femur mesh 626 into the MRI scandata (target image space). In one embodiment, 3D B-Spline deformabletransforms may be employed.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. For example, a 3D B-splinedeformable transform T may specify a 3D vector V(P) for every point P inthe original 3D space that is moved by T such that T:P→P+V(P).

In one embodiment, a B-Spline transformation may be specified with M×Nparameters, where M is the number of nodes in the B-Spline grid and N isthe dimension of the space. In one embodiment, a 3D B-Spline deformabletransformation of order three may be used to map every reference image3D point into the target MRI scan by a different 3D vector. The field ofvectors may be modeled using B-splines. Typically a grid J×K×L ofcontrol points may be specified where J, K, and L are parameters of thetransformation.

In one embodiment, splines of order three may be used with a grid27×9×11 of control points. That is, the transformation employs 27control points in the medial/lateral direction (i.e., the x direction),9 control points in posterior/anterior direction, and 11 control pointsin distal/proximal direction. Two control points in each dimension(i.e., 2 of 27 in the x direction, 2 of 9 in the y direction and 2 of 11in the z direction) may be used to specify boundary conditions. As such,the inner spline nodes may form a grid of size 25 by 7 by 9 and theboundary conditions increase the grid to size 27 by 9 by 11. Theparametric set for this transformation has a dimension of 3×27×9×11=8019(i.e., at each node of a 27×9×11 grid of control points, there isspecified a 3-dimensional transformation vector; a nonlinearinterpolation of transformation vectors for points located betweenadjacent nodes, is governed by spline equations.) The final parametersof the spline transformation that minimizes the misalignment between thereference golden femur template and the target MRI scan data may befound.

In operation 770 e of FIG. 37 a different metric (or defect function)may be used as compared to what was used in operations 770 a, 770 b, and770 c. Specifically, a combined defect function may be used. Thecombined defect function may be defined as a linear combination of thedefect function D (same as in operations 770 a, 770 b, and 770 c) anddefect functions D_i that evaluate the discrepancy between the goldenmesh regions 628, 629 and the scan image edges defined in operation 770d of FIG. 37.

The defect function D_i, or rather its opposite metric functionM_i=−D_i, for a given Golden Mesh Region R_i may be defined as follows.All the vertices in the golden mesh region R_i, are taken, a transformis applied to them, and the correspondent intensities are evaluated inthe edges image. M_i may be set to be the sum of those intensities.Thus, when more vertices from the transformed golden mesh region R_icome close to the image edges, a higher metric value is the result.

When defining the combined metric or defect, that is when taking thelinear combination of D and all the D_i, the coefficients in the linearcombination need to be specified. It may be desirable to use a very highcoefficient with D because we want to follow very precisely thelandmarks 777 provided by a user. Smaller coefficients may be employedwith D_i. The latter coefficients might be also different. The highercoefficients may be used for those regions of the bone that require agreater degree of precision, the associated image segmentation needingto result in more clearly defined regions. The lower coefficients may beused for those regions of the bone that do not require a high degree ofprecision, the associated image segmentation resulting in less clearlydefined regions.

Some bones may have a higher degree of shape variations across thepopulation than is found with the knee region of the femur. For example,the shape of the tibia may vary more from patient to patient than doesthe shape of the femur. As a result, the affine transformation may notprovide a close enough registration of the golden tibia template to thetarget tibia in the target scan. This may cause the Splinetransformation to find a local optimum that may be far from the actualtibia in some areas. In one embodiment, an additional registrationoperation between the affine transform and spline transform operationsmay be performed to more closely align the golden tibia and the targettibia, allowing the spline transform to converge to the correct localoptimum rather than a nearby, but wrong, local optimum.

The class of transforms utilized generally should allow more flexibility(or degrees of freedom) than the Affine transform and less flexibilitythan the B-spline transforms. The number of degrees of freedom generallyis equal to the number of transform parameters. In one embodiment, aclass of transforms with more than 12 parameters and less than 3×27×9×11parameters may be used. For example, a B-spline transform with fewercontrol points than used in the subsequent spline transform may be usedfor the additional transform operation. Alternatively, the deformationsmay be modeled using quadric rather than cubic functions.

In another embodiment, several golden tibia templates may be used thatrepresent typical tibia variations, e.g., golden tibia templates forvarus, valgus, and normal tibia. In one embodiment, each of the goldentibia templates may be used during the translation, similarity andaffine transform registration operations to find the template thatprovides the best match (e.g., best correlation) in the affine transformregistration operation. This template may then be used in the remainingregistration operations.

Finally, in one embodiment, the tibia registration may be improved byperforming the tibia segmentation after the femur segmentation andadding a restriction on the tibia registration transformations such thatthe tibia may not penetrate the femur. In one embodiment, this may beimplemented by introducing a penalty for the penetration. In the targetMRI all the voxels that lie inside the femur segmentation curves may bemarked. The metric functions, described in more detail below, that areused in the registration operations may be modified to include a penaltyterm. The penalty term may be computed by taking points in the goldentibia mesh, applying a transform to the set of points, determining if atransformed sample point falls into any of the marked voxels, and addinga large value to the penalty term for each transformed sample point thatfalls into any of the marked voxels.

In each of the above registration operations, a metric may be used toquantify the degree of correspondence between the reference objects andtarget image achieved by a given transformation. In one embodiment, themetric quantitatively measures how well the transformed golden femurdata fits the target image (e.g., a target MRI scan) and landmarkspositioned there.

As discussed above, metrics M=−D, M_i=−D_i, and their linear combinationare used in operations 770 a-770 d of the registration. An explanationis now given regarding the details on how to compute those metrics. Asfar as using those metrics with optimizers that require computations ofthe gradient of the metric, it is also explained how to compute thegradient of those metrics.

When computing the metric M or rather the defect D, such a computationcan include finding the sum of the squared distances from each landmarkpoint 777 to the transformed open golden mesh. In order to make thiscomputation as quickly and efficiently as possible, the following can bedone. First, a B-Spline transformation of a mesh is no longer a mesh.The plane triangles forming the original mesh get curved over thetransformation, and the triangles are no longer planar. Rather thancomputing distances to curved triangles, which would be verycomputationally expensive, planar triangles connecting the transformedvertices are used. Very little precision is lost with this substitutionbecause the triangles are very small.

Next, after finding the transformed mesh, it is desirable for everyLandmark point to find the closest point in the transformed meshtriangles and take the squared distance to it. A spatial subdivisionscheme is used to sort all the triangles by spatial location. An octreesubdivision is used, although other schemes (kd-tree, fixed size grid,etc.) would work as well. The spatial subdivision helps to find aclosest mesh triangle and a closest point in it using an order of LOG(n)operations where n is the number of triangles in the mesh.

The optimizers used in the registration steps require the computation ofthe gradient of the metric function, which depends on the appliedtransform, over the transform parameters.

In one embodiment the metric function may be a composition of severalfunctions. For the metric M (or cost function D), for example, thefollowing functions are used in the composition: a) mesh transformation,b) distance from a Landmark point to the transformed mesh, c) squareddistance, d) sum of squares, e) inverse of the sum.

For a composition of functions, determining the gradient involvesfinding partial derivatives for each function and then applying thechain rule. The derivatives of the algebraic functions are computed bystandard formulae. The only non-trivial computation in the abovefunctions is the computation of the partial derivative of a distancefrom a point to the transformed mesh.

For the latter computation, it may involve using an approximate method.Namely, take the closest triangle found in the metric computationfollowed by taking the plane containing that triangle. This planeapproximates the transformed mesh surface in some small neighborhood ofthe closest point. One of the transform parameters is changed by a verysmall amount. It is observed where the former closest triangle is mappedafter the variation.

The plane containing the varied triangle is taken. This planeapproximates the varied transformed mesh surface in some smallneighborhood of the varied closest point. The distance from the landmarkpoint to this varied plane is taken. It is approximately the distancefrom the landmark point to the whole varied transformed mesh surface.Now the difference between the varied distance and the original distanceis taken and divided by the value of the parameters variation. Thisgives approximately the partial derivative for this parameter.

In order to compute the gradient of the metric D_i with respect to thetransform parameters, the gradient image of the edges image is computedright after the computation of the edges image itself. To compute thepartial derivative of D_i over a transform parameter, the computationmay take place for the derivative of every transformed vertex motionover that parameter using the chain rule. This derivative will be a 3Dvector. Its dot product is taken with the correspondent Gradient vectorof the Gradient Image of the Edges Image and the values are summed allover the vertices.

Finally, since the combined defect function is a linear combination ofdefect functions D and D_i, then the gradient of the combined defectfunction with respect to a given transform, is correspondingly a linearcombination (with the same coefficients) of the gradients of D and D_iwith respect to that same transform.

In summary and as can be understood from the immediately precedingdiscussion regarding operations 770 a-770 c and 770 e of FIG. 37,translation transforms (operation 770 a), similarity transforms(operation 770 b), affine transforms (operation 770 c), and B-Splinedeformable transforms (operation 770 e) are employed as part ofaccomplishing operation 770 of FIG. 36. Because in operations 770 a-770c of FIG. 37 it is intended to register the open golden femur mesh tolandmarks, the metric (or defect) function should evaluate how close thetransformed open golden femur mesh is to landmarks. Thus, in operations770 a-770 c, there is a selection of the defect function D to be the sumof squared distances from landmarks to the deformed open golden mesh. Inthe operation 770 e, a simultaneous registering of several parametersmay be defined as a combined metric that will take into account all theparameters. The combined defect function may be defined as a linearcombination of the defect function D (same as in operations 770 a-770 c)and defect functions D_i that evaluate the discrepancy between thegolden mesh regions and the scan image edges defined in operation 770 dof FIG. 37.

Once operation 770 of FIG. 36 is completed, the process of FIG. 36 thencontinues with operation 772, wherein the deformed open golden femurmesh 626 and associated regions 628, 629 are segmented followed byoperation 773, wherein the resulting segmentation curves areapproximated with splines. The process continues with operation 774,wherein the contour lines or splines generated in operation 773 aremodified to match landmarks.

In other words, in operation 774 the segmentation curves are refined tomore precisely match the landmarks. Typically, the segmentation curvescreated via the above-described algorithms match the landmarks quiteclosely. Accordingly, most any simple algorithm for a local curveadjustment can work to further refine the precision of the match of thesegmentation curves with the landmarks.

In one embodiment of operation 774 when further refining thesegmentation curves to match landmarks, only those curves that belong toslices that contain landmarks are considered. When a curve belongs to aslice with landmarks, it is assumed that it should rather precisely gothrough all the Landmarks. In one embodiment, a curve may be consideredto be precisely enough located relative to a landmark if its distancefrom the landmark (“Tol”) is Tol=0.3 mm or less. Most often all thelandmarks are within the Tol distance from the curve. However, sometimesa few of the landmarks are further than the Tol distance from the curve.As can be understood from the following discussion regarding operation774, for every curve generated via the above-described algorithms, eachlandmark in a slice is iterated. If a landmark is not within Toldistance from the curve, a correction algorithm is applied to the curveas described below with respect to operation 774.

Operation 774 locally modifies the spline curve to fit a selectedlandmark. Specifically, as can be understood from FIG. 39, which is aflowchart illustrating the process of operation 774 of FIG. 36 in thecontext of a particular image slice containing landmarks and asegmentation contour, in operation 774 a a landmark 777 is identified.In operation 774 b, the distance of the identified landmark 777 to thespline generated from the golden femur mesh 626 is computed. Morespecifically, the algorithm of operation 774 first computes distancesfor all the other landmarks in the same slice to avoid making thedistance relationships of the landmarks and curve worse.

In operation 774 c, an arc of the contour line or spline that is theclosest to the landmark is identified. Specifically, the closest splinearc [A, B] to the selected landmark L is located, where A and B areconsecutive vertices in the spline curve.

In operation 774 d, the arc is modified to include the identifiedlandmark, resulting in a modified contour line or spline. Specifically,the vertices A and B are moved iteratively so that the arc [A, B] fitsL. For each iteration, the closest point C in [A, B] to L is found. Theratio α:(1−α) is then found in which C divides [A, B]. Next, A is movedby (1−α)*(L−C), and B is moved by α*(L−C). The process stops when0.5*Tol distance is achieved.

In operation 774 e, distances of other landmarks to the modified splineare computed and reviewed in operation 774 f to verify operations 774a-774 d have not made the fit between the spline and other landmarksworse. In other words, the following is checked for every landmark.First, it is checked to see if the new distance between the spline andlandmark is within Tol and, if it is, then the relationship between thespline and landmark is acceptable. Second, it is checked to see if thenew distance between the spline and landmark is smaller than the olddistance and, if it is, then the relationship between the spline andlandmark is acceptable. Third, it is checked to see if the new distanceis higher than Tol, the old distance was higher than Tol, and the newdistance increased by less than 0.5*Tol. If the answer is yes withrespect to all three of the elements of the third check, then therelationship between the spline and landmark is acceptable. For all theother cases, the relationship between the spline and landmark is notacceptable. If the distance relationships between the spline and all ofthe landmarks are considered acceptable, the process outlined in FIG. 39is completed for the identified landmark, and the process of FIG. 39 canthen be run for another identified landmark until all landmarks havegone through the process of FIG. 39.

If any of the distance relationships between any landmark and the splineare found to be unacceptable in operation 774 f due to a modification ofthe spline with respect to a selected landmark according to operations774 a-774 d, then per operation 774 g the spline modification fromoperation 774 d is disregarded and a more local modification isemployed, as described below with respect to operations 774 h-774 k ofFIG. 39. The more local modification will add a new vertex into thespline making the spline more flexible in this region. The more localmodification will then move the new vertex to L, and this will affect avery small area of the spline. Thus, the chance of decreasing the fit toother landmarks will be very small. The more local modification occursas follows.

In operation 774 h, a point in the identified arc closest to theidentified landmark is identified. Specifically, the point C in arc [A,B] that is the closest to landmark L is found.

In operation 774 i, a spline vertex is inserted at the identified pointC. With the insertion of a new vertex, the spline shape usually changesin the two immediately adjacent neighbor arcs on both sides of the arc[A, B]. As a result, the arc spline can become too wavy in the vicinityof the arc [A, B].

To remedy the situation, the arc [A, B] is adjusted to fit the originalspline in operation 774 j. Specifically, the vertices A and B aremodified to try to fit the new spline as closely as possible to theoriginal spline. In doing so, a measure of closeness (i.e., how closelythe new spline follows the original spline in the six neighboringarcs—three to each side of the new control point C) may be computed asfollows. In one embodiment, the six spline arcs are sampled such thatthere are twenty or so sample points in every arc of the spline (i.e.,20*6 sample points). Then, the sum of the squared distances from thesample points to the original spline may be computed. Next, thecoordinates of the A and B vertices (control points) are varied (i.e.,two parameters for each of A and B, that is four parameters). Then, alocal optimization algorithm is used to find the closest spline. Thisprocess may be similar to the process of fitting a spline to a polyline,as described elsewhere in this Detailed Description.

Per operation 774 k, the identified point is moved to the identifiedlandmark. Specifically, the spline vertex C is moved into the landmarkpoint L.

The process outlined in FIG. 39 is completed for the identifiedlandmark, and the process of FIG. 39 can then be run for anotheridentified landmark until all landmarks have gone through the process ofFIG. 39.

Once the process of FIG. 39 is completed for all landmarks and theassociated contour lines or splines, the process of operation 774 ofFIG. 36 is considered complete, which completes the process of FIG. 36for the operation 252 a of FIG. 33. The process of operation 252 a inFIG. 33 is now complete. The image slices 16 are then scrolled over toverify if the segmentation results are acceptable, as indicated byoperation 252 c. In operation 253, if the segmentation is acceptable,then the segmentation process of FIG. 33 ends.

As can be understood from FIG. 33, if in operation 253 the segmentationis not acceptable, then the segmentation of each offending slice 16 ismodified by adding additional landmarks 777 and/or modifying thelocations of existing landmarks 777 per operation 254 of FIG. 33. Forexample and as can be understood from FIG. 40, a first spline 800, whichis generated via a first run through operation 252 of FIG. 33, hascontrol points 802 and extends along first landmarks 777 a placed in theslice 16 of FIG. 40 during operation 251 of FIG. 33.

During operation 253 of FIG. 33 the segmentation of the slice 16 of FIG.40 is identified as being unsatisfactory in the location called out byarrow A in FIG. 40. A new landmark 777 b is added in the location calledout by arrow A per operation 254 and operation 252, or morespecifically, operations 774 b-774 e of the algorithm of FIG. 39, arerepeated to generate a second spline 804, which has control points 806and extends along both the first landmarks 777 a and the second landmark777 b. As can be understood from FIG. 40, the first spline 800 and thesecond spline 804 are generally identical and coextensive, except in theregion identified by arrow A. The segmentation of the second spline 804is then approved or disapproved per operation 253. If approved, then thesegmentation process of FIG. 33 ends. If disapproved, then the secondspline 804 is further modified per operation 254 in a manner similar toas discussed above with respect to FIG. 40.

In one embodiment of operation 254 of FIG. 33, the spline may besimultaneously modified near a new added landmark or near movinglandmarks to fit the moving landmarks. In doing so, it may be the casethat the user is satisfied with the corrected splines. As a result, theprocess of FIG. 33 may simply end at operation 254 as if the entirety ofoperation 252 had been completed and the segmentation was foundacceptable at operation 253.

In one embodiment, when a user adds a new landmark into a slice with aspline, the spline is immediately modified using precisely the samealgorithm of FIG. 39, namely operations 774 b-774 e. When a user moves alandmark, the spline is updated during the motion using operations 774b-774 e of the algorithm of FIG. 39. Adding landmarks (operations 774g-774 k of the algorithm of FIG. 39) is avoided during the motion phaseas it may lead to multiple updates during motions, resulting in too manypoints.

Once the contour lines or splines are successfully segmented from eachtarget image slice, the contour lines or splines are compiled asdiscussed above into a 3D mesh that may be used as an arthritic bonemodel 36 (see FIG. 1D) or bone models 22 (see FIG. 1C).

In one embodiment of the registration process discussed above, anoptimizer may be used during the registration process to maximizesimilarity between the open golden mesh and landmarks in the targetimage (and possibly edges image) by adjusting the parameters of a giventransformation model to adjust the location of reference imagecoordinates in the target image. In one embodiment, the optimizer for aregistration operation may use the transformed golden femur data fromthe previous registration operation as its initial approximation. Then,local optimization techniques may be used to search for a local optimumnear the initial starting approximation. This may be done so that anypotential matches farther away from the feature of interest (e.g., thefemur or tibia in a knee joint) reliably found in an earlier operationmay be eliminated.

In operation 770 a of FIG. 37, when optimizing the translationtransformation, a regular step gradient descent optimizer may be used byone embodiment. Other embodiments may use different optimizationtechniques.

To find a local minimum, parameter steps may be taken in the directionof the negative of the metric gradient (or the approximate gradient)over the transform parameter space at the current point. This generallyoptimizes the metric which typically has a local minimum when featuresof the reference image mapped into corresponding features of the targetimage have minimal misalignment).

In one embodiment, initial gradient step of 3 millimeters may bespecified, a relaxation factor may be set to 0.95 and a maximum of 50iterations may be used in the regular step gradient descent optimizationmethod to determine the parameters of the translation transformationthat results in minimal misalignment between the reference Open GoldenFemur mesh and the Landmarks in the target MRI scan.

In operation 770 b of FIG. 37, when optimizing the similaritytransformation, a regular step gradient descent optimizer may be usedagain by one embodiment. When applying the regular step gradient descentoptimizer to similarity transformation, the result and the convergencerate depend on the proper choice of parameters representing thesimilarity transforms. A good choice of parameters when used withgradient computations is such that a variation of every parameter by oneunit leads to approximately equal displacement of object points. Inorder to ensure similar displacement of points with respect to threerotational parameters in the similarity transform, the initial center ofrotation for the similarity transformation may be specified as thecenter of a bounding box (or minimum sized cuboid with sides parallel tothe coordinate planes) that encloses the feature (e.g., a bone)registered in the translation registration (e.g., operation 770 a inFIG. 37). For knee segmentation, scaling coefficients of approximately40-millimeters may be used for the scaling parameters when bringing therotational angle parameters together with translation parameters. Ascaling coefficient of approximately 40-millimeters may be used becauseit is approximately half the size of the bone (in the anterior/posteriorand medial/lateral directions) of interest and results in a point beingmoved approximately 40-millimeters when performing a rotation of oneradian angle. By the same reason a scaling coefficient of 40 millimetersmay be used in the similarity transform scaling parameter together withits translational parameters.

In one embodiment, an initial gradient step of 1.5 millimeters may bespecified, a relaxation factor may be set to 0.95 and a maximum of 50iterations may be performed in the regular step gradient descentoptimization method to determine the parameters of the similaritytransformation that results in minimal misalignment between thereference open golden template mesh and landmarks in the target MRIscan.

In operation 770 c of FIG. 37, when optimizing the affinetransformation, a regular step gradient optimizer may be used again byone embodiment. For knee bones, scaling coefficients of approximately 40millimeters may be used for the matrix coefficients variations whenbringing them together with translation parameters. An initial gradientstep of 1 millimeter may be specified, the relaxation factor may be setto 0.95 and a maximum of 50 iterations may be performed to determine theparameters of the affine transformation that results in minimalmisalignment.

In operation 770 e of FIG. 37, when optimizing the B-splinetransformation, a modified regular step gradient descent optimizer maybe used by one embodiment when searching for the best B-splinedeformable transformation. Namely, a combination of regular stepgradient descent optimizer with by coordinate descent may be used here.

Rather than computing one gradient vector for the transform space andtaking a step along it, a separate gradient may be computed for everyB-spline transform node. In one embodiment, order three B-splines (withJ×K×L control nodes) may be used and J×K×L gradients may be computed,one for each control point. At every iteration, each of the spline nodesmay be moved along its respective gradient. This may enable fasterconvergence of the optimization scheme. A relaxation factor of 0.95 maybe used for each spline node. A an initial gradient step ofone-millimeter may be set for every B-spline grid node, and a maximum of50 iterations may be used in the regular step gradient descentoptimization method to find the parameters of the B-splinetransformation that provides minimal misalignment of the open goldenfemur mesh and landmarks and feature edges in the target MRI scan.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves outlining the surface of an object of interest in eachtarget MRI slice (e.g., as discussed above with respect to operation 772of FIG. 36) after the transformed golden femur mesh is found inoperation 770 e in FIG. 37. Initially, operation 470 intersects thetransformed golden femur mesh with a slice of the target scan data. Theintersection defines a polyline curve of the surface of the feature(e.g., bone) in each slice. Two or more polyline curves may be generatedin a slice when the bone is not very straightly positioned with respectto the slice direction.

A polyline curve is a piecewise linear approximation to a curved featureshape. Generally, this curve should be easy to manipulate with a set ofcontrol points. The polyline curve may have many segments, making itmore difficult to manipulate the polyline curve (e.g., during operation254 or 260 of FIG. 6). One embodiment may generate one or more Kochaneksplines from the polyline curve. Each spline typically has a smallernumber of control points and typically fits the polyline curve withabout 0.3-millimeter deviation. See previous description in thisDetailed Description for a detailed discussion regarding splinegeneration.

As discussed above, in one embodiment, the output of the segmentationmay be a triangular mesh (e.g., a 3D surface model) of the segmentedbone(s) of a joint (e.g., the femur and tibia of a knee joint). The meshgenerated generally represents a watertight surface that closely followsthe segmentation contour curves of the slices, smoothly interpolatesbetween the segmentation contour curves, and may have a low triangularcount. See previous description in this Detailed Description for adetailed discussion regarding mesh generation and the manual adjustmentof segmentation splines.

The 3D surface models of the lower end of the femur and the upper end ofthe tibia of a patient's knee may be used to create arthroplasty jigsand/or implants. For example, the models may be used to create femur andtibia jigs that can be used with a patient's femur and tibia asdisclosed in the various U.S. Patent applications incorporated byreference herein in this Detailed Description and filed by Park and Parket al. The automatic or semi-automatic processes described herein forsegmentation of image data to generate 3D bone models may reduce theoverall time required to perform a reconstructive surgery to repair adysfunctional joint and may also provide improved patient outcomes.

III. Overview of Bone Model Restoration or Modification Process

The description in Section II. focused on the acquisition of medicalimages, the segmentation or auto-segmentation of the medical images, andthe generation of a patient bone model from the segmented images that isrepresentative of the bones of the patient in a deteriorated ordegenerated state. Beginning in Section III., the present disclosureincludes a description of exemplary methods of modifying image data(e.g., 2D image slices) into restored image data (e.g., restored 2Dimage data), and then generating a restored bone model representing apatient's bone in a pre-deteriorated or pre-degenerated state. Therestored bone model or the restored image data (e.g., restored 2D imagedata) may then be used in implant planning (e.g., determining coordinatelocations for resections, implant sizes), as will be described inSection IV.

As mentioned above with respect to [block 115] of FIG. 1C, the processfor restoring damaged regions of 3D “bone models” 22 to generate 3D“restored bone models” 28 can be automated to be carried out to agreater or lesser extent by a computer. A discussion of various examplesof such an automated process will be described, beginning with anoverview of various automated bone restoration processes.

As can be understood from FIG. 1A and [blocks 100-105] of FIG. 1B, apatient 12 has a joint 14 (e.g., a knee, elbow, ankle, wrist, shoulder,hip, vertebra interface, etc.) to be replaced (e.g., partially ortotally) or resurfaced. The patient 12 has the joint 14 scanned in animaging machine 10 (e.g., a CT, MRI, etc. machine) to create a pluralityof 2D scan images 16 of the bones (e.g., femur 18 and tibia 20) formingthe patient's joint 14 (e.g., knee). The process of creating the 2D scanimages or slices 16 may occur as disclosed in Ser. No. 11/946,002, whichwas filed by Park Nov. 27, 2007 and is incorporated by reference in itsentirety into this Detailed Description. Each scan image 16 is a thinslice image of the targeted bone(s) 18, 20. The scan images 16 are sentto the CPU 7, which may employ an open-loop or closed-loop imageanalysis along targeted features 42 of the scan images 16 of the bones18, 20 to generate a contour line for each scan image 16 along theprofile of the targeted features 42. The process of generating contourlines for each scan image 16 may occur as disclosed in Ser. No.11/959,344, which is incorporated by reference in its entirety into thisDetailed Description.

As can be understood from FIG. 1A and [block 110] of FIG. 1C, the CPU 7compiles the scan images 16 and, more specifically, the contour lines togenerate 3D computer surface or volumetric models (“bone models”) 22 ofthe targeted features 42 of the patient's joint bones 18, 20. In thecontext of total knee replacement (“TKR”) or partial knee replacementsurgery, the targeted features 42 may be the lower or knee jointportions of the patient's femur 18 and the upper or knee joint portionsof the patient's tibia 20. More specifically, for the purposes ofgenerating the femur bone models 22, the targeted features 42 mayinclude the condyle portion of the femur and may extend upward toinclude at least a portion of the femur shaft. Similarly, for purposesof generating the tibia bone models 22, the targeted features 42 mayinclude the plateau portion of the tibia and may extend downward toinclude at least a portion of the tibia shaft.

In some embodiments, the “bone models” 22 may be surface models orvolumetric solid models respectively formed via an open-loop orclosed-loop process such that the contour lines are respectively open orclosed loops. Regardless, the bone models 22 are bone-only 3D computergenerated models of the joint bones that are the subject of thearthroplasty procedure. The bone models 22 represent the bones in thedeteriorated condition in which they existed at the time of the medicalimaging of the bones.

To allow for the POP procedure, wherein the saw cut and drill holelocations 30, 32 are determined as discussed with respect to [block 120]of FIG. 1C, the “bone models” 22 and/or the image slices 16 (see [block100] of FIG. 1B) are modified to generate a 3D computer generated modelthat approximates the condition of the patient's bones prior to theirdegeneration. In other words, the resulting 3D computer generated model,which is termed a “restored bone model” 28, approximates the patient'sbones in a non-degenerated or healthy state and can be used to representthe patient's joint in its natural alignment prior to degeneration.

In one embodiment, the bone restoration process employed to generate therestored bone model 28 from the bone model 22 or image slices 16 may beas indicated in the process diagram depicted in FIG. 41. As shown inFIG. 41, the damaged and reference sides of a joint bone to undergo anarthroplasty procedure are identified from the 3D computer generated“bone model” [block 200]. The damaged side is the side or portion of thejoint bone that needs to be restored in the bone model 22, and thereference side is the side of the joint bone that is generally undamagedor at least sufficiently free of deterioration that it can serve as areference for restoring the damaged side.

As can be understood from FIG. 41, reference data or information (e.g.,in the form of ellipses, ellipse axes, and/or vectors in the form oflines and/or planes) is then determined from the reference side of thejoint bone [block 205]. The reference information or data is thenapplied to the damaged side of the joint bone [block 215]. For example,in a first embodiment and in the context of a knee joint, a vectorassociated with a femur condyle ellipse of the reference side isdetermined and applied to the damaged side femur condyle and damagedside tibia plateau. In a second embodiment and in the context of a kneejoint, a vector associated with the highest anterior and posteriorpoints of a tibia plateau of the reference side is determined andapplied to the damaged side femur condyle and damaged side tibiaplateau. These vectors are generally parallel with the condyle ellipseand generally parallel with the knee joint line.

As indicated in FIG. 41, each joint contour line associated with a 2Dimage slice of the damaged side of the joint bone is caused to extend tothe reference vector or ellipse [block 220]. This restoration process iscarried out slice-by-slice for the joint contour lines of most, if notall, image slices associated with the damaged side of the joint. The 3D“bone model” is then reconstructed into the 3D “restored bone model”from the restored 2D images slices [block 225].

Once generated from the “bone model” 22, the “restored bone model” 28can then be employed in the POP process discussed with respect to [block120] of FIG. 1C. As discussed with respect to [blocks 125 and 150], “sawcut and drill hole data” resulting from the POP process is indexed into“jig data” 46 to create “integrated jig data” 48. As discussed withrespect to [blocks 155-165] of FIG. 1E, the “integrated jig data” 48 isutilized by a CNC machine 10 to produce customized arthroplasty jigs 2.

The systems 4 and methods disclosed herein allow for the efficientmanufacture of arthroplasty jigs 2 customized for the specific bonefeatures of a patient. Each resulting arthroplasty jig 2 includes aninterior portion dimensioned specific to the surface features of thepatient's bone that are the focus of the arthroplasty. Each jig 2 alsoincludes saw cut slots and drill holes that are indexed relative to theinterior portion of the jig such that saw cuts and drill holesadministered to the patient's bone via the jig will result in cuts andholes that will allow joint implants to restore the patient's joint lineto a pre-degenerated state or at least a close approximation of thepre-degenerated state.

Where the arthroplasty is for TKR or partial knee replacement surgery,the jigs will be a femur jig and/or a tibia jig. The femur jig will havean interior portion custom configured to match the damaged surface ofthe lower or joint end of the patient's femur. The tibia jig will havean interior portion custom configured to match the damaged surface ofthe upper or joint end of the patient's tibia.

The jigs 2 and systems 4 and methods of producing such jigs areillustrated herein in the context of knees and TKR or partial kneereplacement surgery. However, those skilled in the art will readilyunderstand the jigs 2 and system 4 and methods of producing such jigscan be readily adapted for use in the context of other joints and jointreplacement or resurfacing surgeries, e.g., surgeries for elbows,shoulders, hips, etc. Accordingly, the disclosure contained hereinregarding the jigs 2 and systems 4 and methods of producing such jigsshould not be considered as being limited to knees and TKR or partialknee replacement surgery, but should be considered as encompassing alltypes of joint surgeries.

A. Overview of the Mechanics of an Accurate Restored Bone Model

An overview discussion of the mechanics of an accurate restored bonemodel 28 will first be given before discussing any of the bonerestoration procedures disclosed herein. While this overview discussionis given in the context of a knee joint 14 and, more particularly, afemur restored bone model 28A and a tibia restored bone model 28B, itshould be remembered that this discussion is applicable to other joints(e.g., elbows, ankles, wrists, hips, spine, etc.) and should not beconsidered as being limited to knee joints 14, but to include alljoints.

As shown in FIG. 42A, which is a coronal view of a distal or knee jointend of a femur restored bone model 28A, points D1, D2 represent the mostdistal tangent contact points of each of the femoral lateral and medialcondyles 300 x, 302 x, respectively. In other words, points D1, D2represent the lowest contact points of each of the femoral lateral andmedial condyles 300 x, 302 x when the knee is in zero degree extension.Line D₁D₂ can be obtained by extending across the two tangent contactpoints D₁, D₂. In this femur restored bone model 28A, line D₁D₂ isparallel or nearly parallel to the joint line of the knee when the kneeis in zero degree extension.

The reference line N1 is perpendicular to line D₁D₂ at point D₁ and canbe considered to represent a corresponding 2D image slice 16 taken alongline N1. The reference line N2 is perpendicular to line D₁D₂ at point D₂and can be considered to represent a corresponding 2D image slice 16taken along line N2. The cross-sectional 2D image slices 16 taken alonglines N1, N2 are perpendicular or nearly perpendicular to the tangentline D₁D₂ and joint line.

As shown in FIG. 42B, which is an axial view of a distal or knee jointend of a femur restored bone model 28A, points P1, P2 represent the mostposterior tangent contact points of each of the femoral lateral andmedial condyles 300 x, 302 x, respectively. In other words, points P1,P2 represent the lowest contact points of each of the femoral lateraland medial condyles 300 x, 302 x when the knee is in 90 degreeextension. Line P₁P₂ can be obtained by extending across the two tangentcontact points P1, P2. In this femur restored bone model 28A, line P₁P₂is parallel or nearly parallel to the joint line of the knee when theknee is in 90 degree flexion.

The reference line N3 is perpendicular to line P₁P₂ at point P₁ and canbe considered to represent a corresponding 2D image slice 16 taken alongline N3. In some instances, the lines N1, N3 may occupy generally thesame space on the femur restored bone model 28A or lines N1, N3 may beoffset to a greater or lesser extent from each other along the jointline of the knee. The reference line N4 is perpendicular to line P₁P₂ atpoint P2 and can be considered to represent a corresponding 2D imageslice 16 taken along line N4. In some instances, the lines N2, N4 mayoccupy generally the same space on the femur restored bone model 28A orlines N2, N4 may be offset to a greater or lesser extent from each otheralong the joint line of the knee. The cross-sectional 2D image slices 16taken along lines N3, N4 are perpendicular or nearly perpendicular tothe tangent line P₁P₂ and joint line.

As shown in FIG. 42C, which is a coronal view of a proximal or kneejoint end of a tibia restored bone model 28B, points R₁, R₂ representthe lowest tangent contact points of each of the tibial lateral andmedial plateaus 304 x, 306 x, respectively. In other words, points R₁,R₂ represent the lowest points of contact of the tibia plateau with thefemur condyles when the knee is in zero degree extension. Line R₁R₂ canbe obtained by extending across the two tangent contact points R1, R2.In this tibia restored bone model 28B, line R₁R₂ is parallel or nearlyparallel to the joint line of the knee when the knee is in zero degreeextension. Also, when the knee joint is in zero degree extension, lineR₁R₂ is parallel or nearly parallel to line D₁D₂. When the knee joint isin 90 degree extension, line R₁R₂ is parallel or nearly parallel to lineP₁P₂.

The reference line N1 is perpendicular to line R₁R₂ at point R₁ and canbe considered to represent a corresponding 2D image slice 16 taken alongline N1. The reference line N2 is perpendicular to line R₁R₂ at point R2and can be considered to represent a corresponding 2D image slice 16taken along line N2. The cross-sectional 2D image slices 16 taken alonglines N1, N2 are perpendicular or nearly perpendicular to the tangentline R₁R₂ and joint line. Because both the femur and tibia restored bonemodels 28A, 28B represent the knee joint 14 prior to degeneration ordamage, lines N1, N2 of the femur restored model 28A in FIG. 1A alignwith and may be the same as lines N1, N2 of the tibia restored bonemodel 28B when the knee joint is in zero degree extension. Thus, pointsD₁, D₂ align with points R₁, R₂ when the knee joint is in zero degreeextension.

FIG. 42D represents the femur and tibia restored bone models 28A, 28B inthe views depicted in FIGS. 42A and 42C positioned together to form aknee joint 14. FIG. 42D shows the varus/valgus alignment of the femurand tibia restored bone models 28A, 28B intended to restore thepatient's knee joint 14 back to its pre-OA or pre-degenerated state,wherein the knee joint 14 is shown in zero degree extension and in itsnatural alignment (e.g., neutral, varus or valgus) as the knee jointexisted prior to degenerating. The respective locations of the lateralcollateral ligament (“LCL”) 308 x and medial collateral ligament (“MCL”)310 x are indicated in FIG. 42D by broken lines and serve as stabilizersfor the side-to-side stability of the knee joint 14.

As can be understood from FIGS. 42A, 42C and 42D, when the knee joint 14is in zero degree extension, lines N1, N2 are parallel or nearlyparallel to the LCL 308 x and MCL 310 x. Gap t1 represents the distancebetween the tangent contact point D₁ of the femoral lateral condyle 300x and the tangent contact point R₁ of the tibia lateral plateau 304 x.Gap t2 represents the distance between the tangent contact point D₂ ofthe femoral medial condyle 302 x and the tangent contact point R2 of themedial tibia plateau 306 x. For a properly restored knee joint 14, asdepicted in FIG. 42D, in one embodiment, with respect to varus/valgusrotation and alignment, t1 is substantially equal to t2 such that thedifference between t1 and t2 is less than one millimeter (e.g.,[t1−t2]<<1 mm). Accordingly, line D₁ D₂ is parallel or nearly parallelto the joint line and line R₁R₂.

FIG. 42E represents the femur and tibia restored bone models 28A, 28B inthe views depicted in FIGS. 42B and 42C positioned together to form aknee joint 14. FIG. 42E shows the varus/valgus alignment of the femurand tibia restored bone models 28A, 28B intended to restore thepatient's knee joint 14 back to its pre-OA or pre-degenerated state,wherein the knee joint 14 is shown in 90 degree flexion and in itsnatural alignment (e.g., neutral, varus or valgus) as the knee jointexisted prior to degenerating. The respective locations of the lateralcollateral ligament (“LCL”) 308 x and medial collateral ligament (“MCL”)310 x are indicated in FIG. 42E by broken lines and serve as stabilizersfor the side-to-side stability of the knee joint 14.

As can be understood from FIGS. 42B, 42C and 42E, when the knee joint 14is in 90 degree flexion, lines N3, N4 are parallel or nearly parallel tothe LCL 308 x and MCL 310 x. Gap h1 represents the distance between thetangent contact point P₁ of the femoral lateral condyle 300 x and thetangent contact point R₁ of the tibia lateral plateau 304 x. Gap h2represents the distance between the tangent contact point P₂ of thefemoral medial condyle 302 x and the tangent contact point R2 of themedial tibia plateau 306 x. For a properly restored knee joint 14, asdepicted in FIG. 42E, in one embodiment, with respect to varus/valgusrotation and alignment, h1 is substantially equal to h2 such that thedifference between h1 and h2 is less than one millimeter (e.g.,[h1−h2]<<1 mm). Accordingly, line P₁P₂ is parallel or nearly parallel tothe joint line and line R₁R₂.

FIG. 42F is a sagittal view of the femoral medial condyle ellipse 300 xand, more specifically, the N1 slice of the femoral medial condyleellipse 300 x is taken along line N1 in FIG. 42A. The contour line N₁ inFIG. 42F represents the N1 image slice of the femoral medial condyle 300x. The N1 image slice may be generated via such imaging methods as MRI,CT, etc. An ellipse contour 305 x of the medial condyle 300 x can begenerated along contour line N₁. The ellipse 305 x corresponds with mostof the contour line N₁ for the N1 image slice, including the posteriorand distal regions of the contour line N₁ and portions of the anteriorregion of the contour line N₁. As can be understood from FIG. 42F anddiscussed in greater detail below, the ellipse 305 x provides arelatively close approximation of the contour line N₁ in a region ofinterest or region of contact A_(i) that corresponds to an region of thefemoral medial condyle surface that contacts and displaces against thetibia medial plateau.

As can be understood from FIGS. 42A, 42B and 42F, the ellipse 305 x canbe used to determine the distal extremity of the femoral medial condyle300 x, wherein the distal extremity is the most distal tangent contactpoint D₁ of the femoral medial condyle 300 x of the N1 slice. Similarly,the ellipse 305 x can be used to determine the posterior extremity ofthe femoral medial condyle 300 x, wherein the posterior extremity is themost posterior tangent contact point P₁′ of the femoral medial condyle300 x of the N1 slice. The ellipse origin point O₁, the ellipse majoraxis P₁′PP₁′ and ellipse minor axis D₁DD₁ can be obtained based on theelliptical shape of the N1 slice of the medial condyle 300 x inconjunction with well-known mathematical calculations for determiningthe characteristics of an ellipse.

As can be understood from FIG. 42F and as mentioned above, the region ofcontact A represents or corresponds to the overlapping surface regionbetween the medial tibia plateau 304 x and the femoral medial condyle300 x along the N1 image slice. The region of contact A_(i) for the N1image slice is approximately 120° of the ellipse 305 x of the N1 imageslice from just proximal the most posterior tangent contact point P₁′ tojust anterior the most distal tangent contact point D₁.

FIG. 42G is a sagittal view of the femoral lateral condyle ellipse 302 xand, more specifically, the N2 slice of the femoral lateral condyleellipse 302 x is taken along line N2 in FIG. 42A. The contour line N₂ inFIG. 42G represents the N2 image slice of the femoral lateral condyle302 x. The N2 image slice may be generated via such imaging methods asMRI, CT, etc. An ellipse contour 305 x of the lateral condyle 302 x canbe generated along contour line N₂. The ellipse 305 x corresponds withmost of the contour line N₂ for the N2 image slice, including theposterior and distal regions of the contour line N₂ and portions of theanterior region of the contour line N₂. As can be understood from FIG.42G and discussed in greater detail below, the ellipse 305 x provides arelatively close approximation of the contour line N₂ in a region ofinterest or region of contact A_(i) that corresponds to an region of thefemoral lateral condyle surface that contacts and displaces against thetibia lateral plateau.

As can be understood from FIGS. 42A, 42B and 42G, the ellipse 305 x canbe used to determine the distal extremity of the femoral lateral condyle302 x, wherein the distal extremity is the most distal tangent contactpoint D₂ of the femoral lateral condyle 302 x of the N2 slice.Similarly, the ellipse 305 x can be used to determine the posteriorextremity of the femoral lateral condyle 302 x, wherein the posteriorextremity is the most posterior tangent contact point P₂′ of the femorallateral condyle 302 x of the N2 slice. The ellipse origin point O₂, theellipse major axis P₂′PP₂′ and ellipse minor axis D₂DD₂ can be obtainedbased on the elliptical shape of the N2 slice of the lateral condyle 302x in conjunction with well-known mathematical calculations fordetermining the characteristics of an ellipse.

As can be understood from FIG. 42G and as mentioned above, the region ofcontact A represents or corresponds to the overlapping surface regionbetween the lateral tibia plateau 306 x and the femoral lateral condyle302 x along the N2 image slice. The region of contact A_(i) for the N2image slice is approximately 120° of the ellipse 305 x of the N2 imageslice from just proximal the most posterior tangent contact point P2′ tojust anterior the most distal tangent contact point D₂.

FIG. 42H is a sagittal view of the femoral medial condyle ellipse 300 xand, more specifically, the N3 slice of the femoral medial condyleellipse 300 x is taken along line N3 in FIG. 42B. The contour line N₃ inFIG. 42H represents the N3 image slice of the femoral medial condyle 300x. The N3 image slice may be generated via such imaging methods as MRI,CT, etc. An ellipse contour 305 x of the medial condyle 300 x can begenerated along contour line N₃. The ellipse 305 x corresponds with mostof the contour line N₃ for the N3 image slice, including the posteriorand distal regions of the contour line N₃ and portions of the anteriorregion of the contour line N₃. As can be understood from FIG. 42H anddiscussed in greater detail below, the ellipse 305 x provides arelatively close approximation of the contour line N₃ in a region ofinterest or region of contact A_(i) that corresponds to an region of thefemoral medial condyle surface that contacts and displaces against thetibia medial plateau.

As can be understood from FIGS. 42A, 42B and 42H, the ellipse 305 x canbe used to determine the distal extremity of the femoral medial condyle300 x, wherein the distal extremity is the most distal tangent contactpoint D₁′ of the femoral medial condyle 300 x of the N3 slice.Similarly, the ellipse 305 x can be used to determine the posteriorextremity of the femoral medial condyle 300 x, wherein the posteriorextremity is the most posterior tangent contact point P₁ of the femoralmedial condyle 300 x of the N3 slice. The ellipse origin point O₃, theellipse major axis P₁PP₁ and ellipse minor axis D₁′DD₁′ can be obtainedbased on the elliptical shape of the N3 slice of the medial condyle 300x in conjunction with well-known mathematical calculations fordetermining the characteristics of an ellipse.

As can be understood from FIG. 42H and as mentioned above, the region ofcontact A represents or corresponds to the overlapping surface regionbetween the medial tibia plateau 304 x and the femoral medial condyle300 x along the N3 image slice. The region of contact A_(i) for the N3image slice is approximately 120° of the ellipse 305 x of the N3 imageslice from just proximal the most posterior tangent contact point P₁ tojust anterior the most distal tangent contact point D₁′.

FIG. 42I is a sagittal view of the femoral lateral condyle ellipse 302 xand, more specifically, the N4 slice of the femoral lateral condyleellipse 302 x is taken along line N4 in FIG. 42B. The contour line N₄ inFIG. 42I represents the N4 image slice of the femoral lateral condyle302 x. The N4 image slice may be generated via such imaging methods asMRI, CT, etc. An ellipse contour 305 x of the lateral condyle 302 x canbe generated along contour line N₄. The ellipse 305 x corresponds withmost of the contour line N₄ for the N4 image slice, including theposterior and distal regions of the contour line N₄ and portions of theanterior region of the contour line N₄. As can be understood from FIG.42G and discussed in greater detail below, the ellipse 305 x provides arelatively close approximation of the contour line N₄ in a region ofinterest or region of contact A_(i) that corresponds to an region of thefemoral lateral condyle surface that contacts and displaces against thetibia lateral plateau.

As can be understood from FIGS. 42A, 42B and 42I, the ellipse 305 x canbe used to determine the distal extremity of the femoral lateral condyle302 x, wherein the distal extremity is the most distal tangent contactpoint D₂′ of the femoral lateral condyle 302 x of the N4 slice.Similarly, the ellipse 305 x can be used to determine the posteriorextremity of the femoral lateral condyle 302 x, wherein the posteriorextremity is the most posterior tangent contact point P2 of the femorallateral condyle 302 x of the N4 slice. The ellipse origin point O₄, theellipse major axis P₂PP₂ and ellipse minor axis D₂′DD₂′ can be obtainedbased on the elliptical shape of the N4 slice of the lateral condyle 302x in conjunction with well-known mathematical calculations fordetermining the characteristics of an ellipse.

As can be understood from FIG. 42I and as mentioned above, the region ofcontact A represents or corresponds to the overlapping surface regionbetween the lateral tibia plateau 306 x and the femoral lateral condyle302 x along the N4 image slice. The region of contact A_(i) for the N4image slice is approximately 120° of the ellipse 305 x of the N4 imageslice from just proximal the most posterior tangent contact point P₂ tojust anterior the most distal tangent contact point D₂′.

While the preceding discussion is given in the context of image slicesN1, N2, N3 and N4, of course similar elliptical contour lines, ellipseaxes, tangent contact points and contact regions may be determined forthe other image slices generated during the imaging of the patient'sjoint and which are parallel to image slices N1, N2, N3 and N4.

B. Employing Vectors from a Reference Side of a Joint to a Damaged Sideof a Joint and Extending the Contour Lines of the Damaged Side to Meetthe Vectors to Restore the Damaged Side

A discussion of methods for determining reference vectors from areference side of a joint bone for use in restoring a damaged side ofthe joint bone is first given, followed by specific examples of therestoration process in the context of MRI images. While this overviewdiscussion is given in the context of a knee joint 14 and, moreparticularly, femur and tibia bone models 22A, 22B being converted imageslice by slice into femur and tibia restored bone models 28A, 28B, itshould be remembered that this discussion is applicable to other joints(e.g., elbows, ankles, wrists, hips, spine, etc.) and should not beconsidered as being limited to knee joints 14, but to include alljoints. Also, while the image slices are discussed in the context of MRIimage slices, it should be remembered that this discussion is applicableto all types of medical imaging, including CT scanning.

For a discussion of the motion mechanism of the knee and, morespecifically, the motion vectors associated with the motion mechanism ofthe knee, reference is made to FIGS. 43A and 43B. FIG. 43A is a sagittalview of the lateral tibia plateau 304 x with the lateral femur condyleellipse 305 x of the N1 slice of FIG. 42F superimposed thereon. FIG. 43Bis a sagittal view of the medial tibia plateau 306 x with the lateralfemur condyle ellipse 305 x of the N2 slice of FIG. 42G superimposedthereon.

The motion mechanism for a human knee joint operates as follows. Thefemoral condyles glide on the corresponding tibia plateaus as the kneemoves, and in a walking theme, as a person's leg swings forward, thefemoral condyles and the corresponding tibia plateaus are not under thecompressive load of the body. Thus, the knee joint movement is a slidingmotion of the tibia plateaus on the femoral condyles coupled with arolling of the tibia plateaus on the femoral condyles in the samedirection. The motion mechanism of the human knee as the femur condylesand tibia plateaus move relative to each other between zero degreeflexion and 90 degree flexion has associated motion vectors. Asdiscussed below, the geometrical features of the femur condyles andtibia plateaus can be analyzed to determine vectors U₁, U₂, V₁, V₂, V₃,V₄ that are associated with image slices N1, N2, N3 and N4. Thesevectors U₁, U₂, V₁, V₂, V₃, V₄ correspond to the motion vectors of thefemur condyles and tibia plateaus moving relative to each other. Thedetermined vectors U₁, U₂, V₁, V₂, V₃, V₄ associated with a healthy sideof a joint 14 can be applied to a damaged side of a joint 14 to restorethe bone model 22 to create a restored bone model 28.

In some embodiments of the bone restoration process disclosed herein andas just stated, the knee joint motion mechanism may be utilized todetermine the vector references for the restoration of bone models 22 torestored bone models 28. As can be understood from a comparison of FIGS.42F and 42G to FIGS. 43A and 43B, the U₁ and U₂ vectors respectivelycorrespond to the major axes P₁′PP₁′ and P₂′PP₂′ of the ellipses 305 xof the N1 and N2 slices. Since the major axes P₁′PP₁′ and P₂′PP₂′ existin the N1 and N2 slices, which are planes generally perpendicular to thejoint line, the U₁ and U₂ vectors may be considered to represent bothvector lines and vector planes that are perpendicular to the joint line.

The U₁ and U₂ vectors are based on the joint line reference between thefemur and the tibia from the zero degree flexion (full extension) to 90degree flexion. The U₁ and U₂ vectors represent the momentary slidingmovement force from zero degree flexion of the knee to any degree offlexion up to 90 degree flexion. As can be understood from FIGS. 43A and43B, the U₁ and U₂ vectors, which are the vectors of the femoralcondyles, are generally parallel to and project in the same direction asthe V₁ and V₂ vectors of the tibia plateaus 321 x, 322 x. The vectorplanes associated with these vectors U₁, U₂, V₁, V₂ are presumed to beparallel or nearly parallel to the joint line of the knee joint 14represented by restored bone model 28A, 28B such as those depicted inFIGS. 42D and 42E.

As shown in FIGS. 43A and 43B, the distal portion of the ellipses 305 xextend along and generally correspond with the curved surfaces 321 x,322 x of the tibia plateaus. The curved portions 321 x, 322 x of thetibia plateaus that generally correspond with the distal portions of theellipses 305 x represent the tibia contact regions A_(k), which are theregions that contact and displace along the femur condyles andcorrespond with the condyle contact regions A_(i) discussed with respectto FIGS. 42F-42I.

For a discussion of motion vectors associated with the tibia plateaus,reference is made to FIGS. 43C-43E. FIG. 43C is a top view of the tibiaplateaus 304 x, 306 x of a restored tibia bone model 28B. FIG. 43D is asagittal cross section through a lateral tibia plateau 304 x of therestored bone model 28B of FIG. 43C and corresponding to the N3 imageslice of FIG. of FIG. 42B. FIG. 43E is a sagittal cross section througha medial tibia plateau 306 x of the restored bone model 28B of FIG. 43Cand corresponding to the N4 image slice of FIG. of FIG. 42B.

As shown in FIGS. 43C-43E, each tibia plateau 304 x, 306 x includes acurved recessed condyle contacting surface 321 x, 322 x that isgenerally concave extending anterior/posterior and medial/lateral. Eachcurved recessed surface 321 x, 322 x is generally oval in shape andincludes an anterior curved edge 323 x, 324 x and a posterior curvededge 325 x, 326 x that respectively generally define the anterior andposterior boundaries of the condyle contacting surfaces 321 x, 322 x ofthe tibia plateaus 304 x, 306 x. Depending on the patient, the medialtibia plateau 306 x may have curved edges 324 x, 326 x that are slightlymore defined than the curved edges 323 x, 325 x of the lateral tibiaplateau 304 x.

Anterior tangent lines T_(Q3), T_(Q4) can be extended tangentially tothe most anterior location on each anterior curved edge 323 x, 324 x toidentify the most anterior points Q3, Q4 of the anterior curved edges323 x, 324 x. Posterior tangent lines T_(Q3)′, T_(Q4)′ can be extendedtangentially to the most posterior location on each posterior curvededge 325 x, 326 x to identify the most posterior points Q3′, Q4′ of theposterior curved edges 325 x, 326 x. Such anterior and posterior pointsmay correspond to the highest points of the anterior and posteriorportions of the respective tibia plateaus.

Vector line V3 extends through anterior and posterior points Q3, Q3′,and vector line V4 extends through anterior and posterior points Q4,Q4′. Each vector line V3, V4 may align with the lowest point of theanterior-posterior extending groove/valley that is the ellipticalrecessed tibia plateau surface 321 x, 322 x. The lowest point of theanterior-posterior extending groove/valley of the elliptical recessedtibia plateau surface 321 x, 322 x may be determined via simpleellipsoid calculus. Each vector V3, V4 will be generally parallel to theanterior-posterior extending valleys of its respective ellipticalrecessed tibia plateau surface 321 x, 322 x and will be generallyperpendicular to it respective tangent lines T_(Q3), T_(Q4), T_(Q3)′,T_(Q4)′. The anterior-posterior extending valleys of the ellipticalrecessed tibia plateau surfaces 321 x, 322 x and the vectors V3, V4aligned therewith may be generally parallel with and even exist withinthe N3 and N4 image slices depicted in FIG. 42B.

As can be understood from FIGS. 43A-43E, the V₃ and V₄ vectors, whichare the vectors of the tibia plateaus, are generally parallel to andproject in the same direction as the other tibia plateau vectors V₁ andV₂ and, as a result, the femur condyle vectors U₁, U₂. The vector planesassociated with these vectors U₁, U₂, V₁, V₂, V₃ and V₄ are presumed tobe parallel or nearly parallel to the joint line of the knee joint 14represented by restored bone models 28A, 28B such as those depicted inFIGS. 42D and 42E.

As indicated in FIGS. 43A-43C, tibia plateau vectors V₁ and V₂ in the N1and N2 image slices can be obtained by superimposing the femoral condyleellipses 305 x of the N1 and N2 image slices onto their respective tibiaplateaus. The ellipses 305 x correspond to the elliptical tibia plateausurfaces 321 x, 322 x along the condyle contact regions A_(k) of thetibia plateaus 304 x, 306 x. The anterior and posterior edges 323 x, 324x, 325 x, 326 x of the elliptical tibia plateau surfaces 321 x, 322 xcan be determined at the locations where the ellipses 305 x ceasecontact with the plateau surfaces 321 x. 322 x. These edges 323 x, 324x, 325 x, 326 x are marked as anterior and posterior edge points Q1,Q1′, Q2, Q2′ in respective image slices N1 and N2. Vector lines V1 andV2 are defined by being extended through their respective edge pointsQ1, Q1′, Q2, Q2′.

As can be understood from FIG. 43C, image slices N1, N2, N3 and N4 andtheir respective vectors V₁, V₂, V₃ and V₄ may be medially-laterallyspaced apart a greater or lesser extent, depending on the patient. Withsome patients, the N1 and N3 image slices and/or the N2 and N4 imageslices may generally medially-laterally align.

While the preceding discussion is given with respect to vectors U₁, U₂,V₁, V₂, V₃ and V4, contact regions A_(i), A_(k), and anterior andposterior edge points Q1, Q1′, Q2, Q2′, Q3, Q3′, Q4, Q4′ associated withimage slices N1, N2, N3 and N4, similar vectors, contact regions, andanterior and posterior edge points can be determined for the other imageslices 16 used to generate the 3D computer generated bone models 22 (see[block 100]-[block 110] of FIGS. 1A-1C).

As illustrated via the following examples given with respect to MRIslices, vectors similar to the U₁, U₂, V₁, V₂, V₃, V₄ vectors of FIGS.43A-43E can be employed in restoring image slice-by-image slice the bonemodels 22A, 22B into restored bone models 28A, 28B. For example, a bonemodel 22 includes a femur bone model 22A and a tibia bone model 22B. Thebone models 22A, 22B are 3D bone-only computer generated models compiledvia any of the above-mentioned 3D computer programs from a number ofimage slices 16, as discussed with respect to [blocks 100]-[block 110]of FIGS. 1A-1C. Depending on the circumstances and generally speaking,either the medial side of the bone models will be generally undamagedand the lateral side of the bone models will be damaged, or vice versa.

For example, as indicated in FIG. 43F, which is a posterior-lateralperspective view of femur and tibia bone models 22A, 22B forming a kneejoint 14, the medial sides 302 x, 306 x of the bone models 22A, 22B arein a generally non-deteriorated condition and the lateral sides 300 x,304 x of the bone models 22A, 22B are in a generally deteriorated ordamaged condition. The lateral sides 300 x, 304 x of the femur and tibiabone models 22A, 22B depict the damaged bone attrition on the lateraltibia plateau and lateral femoral condyle. The lateral sides 300 x, 304x illustrate the typical results of OA, specifically joint deteriorationin the region of arrow L_(S) between the femoral lateral condyle 300 xand the lateral tibia plateau 304 x, including the narrowing of thelateral joint space 330 x as compared to medial joint space 332 x. Asthe medial sides 302 x, 306 x of the bone models 22A, 22B are generallyundamaged, these sides 302 x, 306 x will be identified as the referencesides of the 3D bone models 22A, 22B (see [block 200] of FIG. 41). Also,as the lateral sides 300 x, 304 x of the bone models 22A, 22B aredamaged, these sides 300 x, 304 x will be identified as the damagedsides of the 3D bone models 22A, 22B (see [block 200] of FIG. 41) andtargeted for restoration, wherein the images slices 16 associated withthe damaged sides 300 x, 304 x of the bone models 22A, 22B are restoredslice-by-slice.

Reference vectors like the U₁, U₂, V₁, V₂, V₃, V₄ vectors may bedetermined from the reference side of the bone models 22A, 22B (see[block 205] of FIG. 41). Thus, as can be understood from FIGS. 43B and43F, since the medial sides 302 x, 306 x are the reference sides 302 x,306 x, the reference vectors U₂, V₂ V₄ may be applied to the damagedsides 300 x, 304 x to restore the damaged sides 300 x, 304 x 2D imageslice by 2D image slice (see [block 215]-[block 220] of FIG. 41). Therestored image slices are then recompiled into a 3D computer generatedmodel, the result being the 3D computer generated restored bone models28A, 28B (see [block 225] of FIG. 41).

As shown in FIG. 43G, which is a posterior-lateral perspective view offemur and tibia restored bone models 28A, 28B forming a knee joint 14,the lateral sides 300 x, 304 x of the restored bone models 28A, 28B havebeen restored such that the lateral and medial joint spaces 330 x, 332 xare generally equal. In other words, the distance t1 between the lateralfemur condyle and lateral tibia plateau is generally equal to thedistance t2 between the medial femur condyle and the medial tibiaplateau.

The preceding discussion has occurred in the context of the medial sides302 x, 306 x being the reference sides and the lateral sides 300 x, 304x being the damaged sides; the reference vectors U₂, V₂ and V₄ of themedial sides 302 x, 306 x being applied to the damaged sides 300 x, 304x in the process of restoring the damaged sides 300 x, 304 x. Of course,as stated above, the same process could occur in a reversed context,wherein the lateral sides 300 x, 304 x are generally undamaged and areidentified as the reference sides, and the medial sides 302 x, 306 x aredamaged and identified as the damaged sides. The reference vectors U₁,V₁ and V₃ of the lateral sides 300 x, 304 x can then be applied to thedamaged sides 302 x, 306 x in the process of restoring the damaged sides302 x, 306 x.

Multiple approaches are disclosed herein for identifying referencevectors and applying the reference vectors to a damaged side for therestoration thereof. For example, as can be understood from FIGS. 43Band 43F, where the medial sides 302 x, 306 x are the undamaged referencesides 302 x, 304 x and the lateral sides 300 x, 304 x the damaged sides300 x, 304 x, in one embodiment, the ellipses and vectors associatedwith the reference side femur condyle 302 x (e.g., the ellipse 305 x ofthe N2 slice and the vector U₂) can be applied to the damaged side femurcondyle 300 x and damaged side tibia plateau 304 x to restore thedamaged condyle 300 x and damaged plateau 304 x. Alternatively oradditionally, the ellipses and vectors associated with the referenceside femur condyle 302 x as applied to the reference side tibia plateau306 x (e.g., the ellipse 305 x of the N2 slice and the vector V₂) can beapplied to the damaged side femur condyle 300 x and damaged side tibiaplateau 304 x to restore the damaged condyle 300 x and damaged plateau304 x. In another embodiment, as can be understood from FIGS. 43C, 43Eand 43F, the vectors associated with the reference side tibia plateau306 x (e.g., the vector V₄) can be applied to the damaged side femurcondyle 300 x and damaged side tibia plateau 304 x to restore thedamaged condyle 300 x and damaged plateau 304 x. Of course, if theconditions of the sides 300 x, 302 x, 304 x, 306 x were reversed in FIG.43F, the identification of the reference sides, the damaged sides, thereference vectors and the application thereof would be reversed fromexamples given in this paragraph.

1. Employing Vectors from a Femur Condyle of a Reference Side of a KneeJoint to Restore the Femur Condyle and Tibia Plateau of the Damaged Side

For a discussion of a first scenario, wherein the medial sides 302 x,306 x are the damaged sides and the lateral sides 300 x, 304 x are thereference sides, reference is made to FIGS. 44A-44B. FIG. 44A is acoronal view of a femur bone model 22A, and FIG. 44B is a coronal viewof a tibia bone model 22B.

As shown in FIG. 44A, the medial femur condyle 302 x is deteriorated inregion 400 x such that the most distal point of the medial condyle 302 xfails to intersect point D₂ on line D1D2, which will be corrected oncethe femur bone model 22A is properly restored to a restored femur bonemodel 28A such as that depicted in FIG. 42A. As illustrated in FIG. 44B,the medial tibia plateau 306 x is deteriorated in region 401 x such thatthe lowest point of the medial plateau 306 x fails to intersect point R₂on line R₁R₂, which will be corrected once the tibia bone model 22B isproperly restored to a restored tibia bone model 28B such as thatdepicted in FIG. 42C. Because the medial condyle 302 x and medialplateau 306 x of the bone models 22A, 22B are deteriorated, they will beidentified as the damaged sides and targeted for restoration ([block200] of FIG. 41).

As illustrated in FIG. 44A, the lateral condyle 300 x and lateralplateau 304 x of the bone models 22A, 22B are in a generallynon-deteriorated state, the most distal point D₁ of the lateral condyle300 x intersecting line D₁ D₂, and the lowest point R₁ of the lateralplateau 304 x intersecting line R₁R₂. Because the lateral condyle 300 xand lateral plateau 304 x of the bone models 22A, 22B are generally in anon-deteriorated state, they will be identified as the reference sidesand the source of information used to restore the damaged sides 302 x,306 x ([block 200] of FIG. 41).

As can be understood from FIGS. 42F, 43A and 44A, for most if not all ofthe image slices 16 of the lateral condyle 300 x, image sliceinformation or data such as ellipses and vectors can be determined. Forexample, an ellipse 305 x and vector U₁ can be determined for the N1slice ([block 205] of FIG. 41). The data or information associated withone or more of the various slices 16 of the lateral condyle 300 x isapplied to or superimposed on one or more image slices 16 of the medialcondyle 302 x ([block 215] of FIG. 41). For example, as shown in FIG.44C1, which is an N2 image slice of the medial condyle 302 x as takenalong the N2 line in FIG. 44A, data or information pertaining to the N1slice is applied to or superimposed on the N2 image slice to determinethe extent of restoration needed in deteriorated region 400 x. Forexample, the data or information pertaining to the N1 slice may be inthe form of the N1 slice's ellipse 305 x-N1, vector U₁, ellipse axesP₁′PP₁′, D₁DD₁, etc. The ellipse 305 x-N1 will inherently contain itsmajor and minor axis information, and the vector U₁ of the N1 slice willcorrespond to the major axis of the 305 x-N1 ellipse and motion vectorof the femur condyles relative to the tibia plateaus. The major axis ofthe 305 x-N1 and the vector U₁ of the N1 slice are generally parallel tothe joint line plane.

In a first embodiment, the N1 slice information may be applied only tothe contour line of the N2 slice or another specific slice. In otherwords, information of a specific reference slice may be applied to acontour line of a single specific damaged slice with which the specificreference slice is coordinated with via manual selection or an algorithmfor automatic selection. For example, in one embodiment, the N1 sliceinformation may be manually or automatically coordinated to be appliedonly to the N2 slice contour line, and the N3 slice information may bemanually or automatically coordinated to be applied only to the N4 slicecontour line. Other reference side slice information may be similarlycoordinated with and applied to other damaged side slice contours in asimilar fashion. Coordination between a specific reference slice and aspecific damaged slice may be according to various criteria, forexample, similarity of the function and/or shape of the bone regionspertaining to the specific reference slice and specific damaged sliceand/or similarity of accuracy and dependability for the specificreference slice and specific damaged slice.

In a second embodiment, the N1 slice information or the sliceinformation of another specific slice may be the only image slice usedas a reference slice for the contour lines of most, if not all, of thedamaged slices. In other words, the N1 image slice information may bethe only reference side information used (i.e., to the exclusion of, forexample, the N3 image slice information) in the restoration of thecontour lines of most, if not each, damaged side image slice (i.e., theN1 image slice information is applied to the contour lines of the N2 andN4 image slices and the N3 image slice information is not used). In suchan embodiment, the appropriate single reference image slice may beidentified via manual identification or automatic identification via,for example, an algorithm. The identification may be according tocertain criteria, such as, for example, which reference image slice ismost likely to contain the most accurate and dependable referenceinformation.

While the second embodiment is discussed with respect to informationfrom a single reference image being applied to the contour lines ofmost, if not all, damaged side image slices, in other embodiments, thereference information applied to the contour lines of the damaged imageslices may be from more than one image slice. For example, informationfrom two or more reference image slices (e.g., N1 image slice and N3image slice) are applied individually to the contour lines of thevarious damage image slices. In one embodiment, the information from thetwo or more reference image slices may be combined (e.g., averaged) andthe combined information then applied to the contour lines of individualdamaged image slices.

In some embodiments, the reference side data or information may includea distal tangent line DTL and a posterior tangent line PTL. The distaltangent line DTL may tangentially intersect the extreme distal point ofthe reference image slice and be parallel to the major axis of thereference image slice ellipse. For example, with respect to the N1 imageslice serving as a reference side image slice, the distal tangent lineDTL may tangentially intersect the extreme distal point D₁ of thereference N1 image slice and be parallel to the major axis P₁′PP₁′ ofthe reference N1 image slice ellipse 305 x-N1.

The posterior tangent line PTL may tangentially intersect the extremeposterior point of the reference image slice and be parallel to themajor axis of the reference image slice ellipse. For example, withrespect to the N1 image slice serving as a reference side image slice,the posterior tangent line PTL may tangentially intersect the extremeposterior point P₁ of the reference N1 image slice and be parallel tothe minor axis D₁DD₁ of the reference N1 image slice ellipse 305 x-N1.

As can be understood from FIGS. 42F-42I, most, if not all, femur condyleimage slices N1, N2, N3, N4 will have an origin O₁, O₂, O₃, O₄associated with the ellipse 305 x used to describe or define the condylesurfaces of each slice N1, N2, N3, N4. When these image slices arecombined together to form the 3D computer generated bone models 22, thevarious origins O₁, O₂, O₃, O₄ will generally align to form a femur axisAO_(F) extending medial-lateral through the femur bone model 22A asdepicted in FIG. 44A. This axis AO_(F) can be used to properly orientreference side data (e.g., the ellipse 305 x-N1 and vector U₁ of the N1slice in the current example) when being superimposed onto a damagedside image slice (e.g., the N2 image slice in the current example). Theorientation of the data or information of the reference side does notchange as the data or information is being superimposed or otherwiseapplied to the damaged side image slice. For example, the orientation ofthe ellipse 305 x-N1 and vector U₁ of the N1 slice is maintained or heldconstant during the superimposing of such reference information onto theN2 slice such that the reference information does not change withrespect to orientation or spatial ratios relative to the femur axisAO_(F) when being superimposed on or otherwise applied to the N2 slice.Thus, as described in greater detail below, since the reference sideinformation is indexed to the damaged side image slice via the axisAO_(F) and the orientation of the reference side information does notchange in the process of being applied to the damaged side image slice,the reference side information can simply be adjusted with respect tosize, if needed and as described below with reference to FIGS. 44C2 and44C3, to assist in the restoration of the damaged side image slice.

While the reference side information may be positionally indexedrelative to the damaged side image slices via the femur reference axisAO_(F) when being applied to the damaged side image slices, other axesmay be used for indexing besides an AO axis that runs through or nearthe origins of the respective image slice ellipses. For example, areference axis similar to the femur reference axis AO_(F) and runningmedial-lateral may pass through other portions of the femur bone model22A or outside the femur bone model 22A and may be used to positionallyindex the reference side information to the respective damaged sideimage slices.

The contour line N₂ of the N2 image slice, as with any contour line ofany femur or tibia image slice, may be generated via an open or closedloop computer analysis of the cortical bone of the medial condyle 302 xin the N2 image slice, thereby outlining the cortical bone with an openor closed loop contour line N₂. Where the contour lines are closed loop,the resulting 3D models 22, 28 will be 3D volumetric models. Where thecontour lines are open loop, the resulting 3D models 22, 28 will be 3Dsurface models.

While in some cases the reference information from a reference imageslice may be substantially similar in characteristics (e.g., size and/orratios) to the damaged image slice contour line to be simply applied tothe contour line, in many cases, the reference information may need tobe adjusted with respect to size and/or ratio prior to using thereference information to restore the damaged side contour line asdiscussed herein with respect to FIGS. 44C1 and 44D. For example, asindicated in FIG. 44C2, which is the same view as FIG. 44C1, exceptillustrating the reference information is too small relative to thedamaged side contour line, the reference information should be increasedprior to being used to restore the damaged side contour line. In otherwords, the N1 information (e.g., the N1 ellipse, vector and tangentlines PTL, DTL), when applied to the contour line of the N2 image slicebased on the AO axis discussed above, is too small for at least some ofthe reference information to match up with at least some of the damagedcontour line at the most distal or posterior positions. Accordingly, ascan be understood from a comparison of FIGS. 44C1 and 44C2, the N1information may be increased in size as needed, but maintaining itsratios (e.g., the ratio of the major/minor ellipse axes to each otherand the ratios of the offsets of the PTL, DTL from the origin or AOaxis), until the N1 information begins to match a boundary of thecontour line of the N2 image slice. For example, as depicted in FIG.44C2, the N1 ellipse is superimposed over the N2 image slice andpositionally coordinated with the N2 image slice via the AO axis. The N1ellipse is smaller than needed to match the contour line of the N2 imageslice and is expanded in size until a portion (e.g., the PTL and P₁′ ofthe N1 ellipse) matches a portion (e.g., the most posterior point) ofthe elliptical contour line of the N2 image slice. A similar process canalso be applied to the PTL and DTL, maintaining the ratio of the PTL andDTL relative to the AO axis. As illustrated in FIG. 44C1, the N1information now corresponds to at least a portion of the damaged imageside contour line and can now be used to restore the contour line asdiscussed below with respect to FIG. 44D.

As indicated in FIG. 44C3, which is the same view as FIG. 44C1, exceptillustrating the reference information is too large relative to thedamaged side contour line, the reference information should be decreasedprior to being used to restore the damaged side contour line. In otherwords, the N1 information (e.g., the N1 ellipse, vector and tangentlines PTL, DTL), when applied to the contour line of the N2 image slicebased on the AO axis discussed above, is too large for at least some ofthe reference information to match up with at least some of the damagedcontour line at the most distal or posterior positions. Accordingly, ascan be understood from a comparison of FIGS. 44C1 and 44C3, the N1information may be decreased in size as needed, but maintaining itsratios (e.g., the ratio of the major/minor ellipse axes to each otherand the ratios of the offsets of the PTL, DTL from the origin or AOaxis), until the N1 information begins to match a boundary of thecontour line of the N2 image slice. For example, as depicted in FIG.44C3, the N1 ellipse is superimposed over the N2 image slice andpositionally coordinated with the N2 image slice via the AO axis. The N1ellipse is larger than needed to match the contour line of the N2 imageslice and is reduced in size until a portion (e.g., the PTL and P₁′ ofthe N1 ellipse) matches a portion (e.g., the most posterior point) ofthe elliptical contour line of the N2 image slice. A similar process canalso be applied to the PTL and DTL, maintaining the ratio of the PTL andDTL relative to the AO axis. As illustrated in FIG. 44C1, the N1information now corresponds to at least a portion of the damaged imageside contour line and can now be used to restore the contour line asdiscussed below with respect to FIG. 44D.

As can be understood from FIG. 44D, which is the N2 image slice of FIG.44C1 subsequent to restoration, the contour line N₂ of the N2 imageslice has been extended out to the boundaries of the ellipse 305 x-N1 inthe restored region 402 x ([block 220] of FIG. 41). This process ofapplying information (e.g., ellipses 305 x and vectors) from thereference side to the damaged side is repeated slice-by-slice for most,if not all, image slices 16 forming the damaged side of the femur bonemodel 22A. Once most or all of the image slices 16 of the damaged sidehave been restored, the image slices used to form the femur bone model22A, including the recently restored images slices, are recompiled via3D computer modeling programs into a 3D femur restored bone model 28Asimilar to that depicted in FIG. 42A ([block 225] of FIG. 41).

As can be understood from FIGS. 44C1 and 44D, in one embodiment, thedamaged contour line N₂ of the N2 image slice is adjusted based on theratio of the reference side major axis major axis P₁′PP₁′ to thereference side minor axis D₁DD₁. In one embodiment, the damaged contourline N₂ of the N2 image slice is adjusted based on reference sideellipse 305 x-N1. Therefore, the damaged contour lines of the damagedside image slices can be assessed to be enlarged according to the ratiospertaining to the ellipses of the reference side image slices.

Depending on the relationship of the joint contour lines of the damagedside image slice relative to the ratios obtained from the reference sideinformation or data, the joint contour lines of the damaged side imageslice may be manipulated so the joint contour line is increased alongits major axis and/or its minor axis. Depending on the patient's kneeshape, the major axis and minor axis of the condyle ellipse varies fromperson to person. If the major axis is close to the minor axis in theundamaged condyle, then the curvature of the undamaged condyle is closeto a round shape. In such configured condyles, in the restorationprocedure, the contour of the damaged condyle can be assessed andincreased in a constant radius in both the major and minor axis. Forcondyles of other configurations, such as where the undamaged condyleshows an ellipse contour with a significantly longer major axis ascompared to its minor axis, the bone restoration may increase the majoraxis length in order to modify the damaged condyle contour.

A damaged side tibia plateau can also be restored by applying data orinformation from the reference side femur condyle to the damaged sidetibia plateau. In this continued example, the damaged side tibia plateauwill be the medial tibia plateau 306 x, and the reference side femurcondyle will be the lateral femur condyle 300 x. In one embodiment, theprocess of restoring the damaged side tibia plateau 306 x begins byanalyzing the damaged side tibia plateau 306 x to determine at least oneof a highest anterior point or a highest posterior point of the damagedside tibia plateau 306 x.

In one embodiment, as can be understood from FIG. 43C as viewed alongthe N4 image slice and assuming the damage to the medial tibia plateau306 x is not so extensive that at least one of the highest anterior orposterior points Q4, Q4′ still exists, the damaged tibia plateau 306 xcan be analyzed via tangent lines to identify the surviving high pointQ4, Q4′. For example, if the damage to the medial tibia plateau 306 xwas concentrated in the posterior region such that the posterior highestpoint Q4′ no longer existed, the tangent line TQ4 could be used toidentify the anterior highest point Q4. Similarly, if the damage to themedial tibia plateau 306 x was concentrated in the anterior region suchthat the anterior highest point Q4 no longer existed, the tangent lineTQ4′ could be used to identify the posterior highest point Q4′. In someembodiments, a vector extending between the highest points Q4, Q4′ maybe generally perpendicular to the tangent lines T_(Q4), T_(Q4)′.

In another embodiment, the reference side femur condyle ellipse 305 x-N1can be applied to the damaged medial tibia plateau 306 x to determine atleast one of the highest anterior or posterior points Q4, Q4′ along theN4 image slice. This process may be performed assuming the damage to themedial tibia plateau 306 x is not so extensive that at least one of thehighest anterior or posterior points Q4, Q4′ still exists. For example,as illustrated by FIG. 44E, which is a sagittal view of the medial tibiaplateau 306 x along the N4 image slice, wherein damage 401 x to theplateau 306 x is mainly in the posterior region, the reference sidefemur condyle ellipse 305 x-N1 can be applied to the damaged medialtibia plateau 306 x to identify the anterior highest point Q4 of thetibia plateau 306 x. Similarly, in another example, as illustrated byFIG. 44F, which is a sagittal view of the medial tibia plateau 306 xalong the N4 image slice, wherein damage 401 x to the plateau 306 x ismainly in the anterior region, the reference side femur condyle ellipse305 x-N1 can be applied to the damaged medial tibia plateau 306 x toidentify the posterior highest point Q4′ of the tibia plateau 306 x.

In one embodiment in a manner similar to that discussed above withrespect to FIGS. 44C2 and 44C3, the reference information (e.g., N1information such as the N1 ellipse) may be applied to the damagedcontour line via the AO axis and adjusted in size (e.g., made smaller orlarger) until the N1 ellipse matches a portion of the damaged contourline in order to find the highest point, which may be, for example, Q4or Q4′. As explained above with respect to FIGS. 44C2 and 44C3, theadjustments in size for reference information may be made whilemaintaining the ratio of the N1 information.

Once the highest point is determined through any of the above-describedmethods discussed with respect to FIGS. 43C, 44E and 44F, the referenceside femur condyle vector can be applied to the damaged side tibiaplateau to determine the extent to which the tibia plateau contour line322 x needs to be restored ([block 215] of FIG. 41). For example, asillustrated by FIGS. 44G and 44H, which are respectively the same viewsas FIGS. 44E and 44F, the vector from the reference side lateral femurcondyle 300 x (e.g., the vector U₁ from the N1 image slice) is appliedto the damaged side medial tibia plateau 306 x such that the vector U₁intersects the existing highest point. Thus, as shown in FIG. 44G, wherethe existing highest point is the anterior point Q4, the vector U₁ willextend through the anterior point Q4 and will spaced apart from damage401 x in the posterior region of the tibia plateau contour line 322 x bythe distance the posterior region of the tibia plateau contour line 322x needs to be restored. Similarly, as shown in FIG. 44H, where theexisting highest point is the posterior point Q4′, the vector U₁ willextend through the posterior point Q4′ and will spaced apart from thedamage 401 x of the anterior region of the tibia plateau contour line322 x by the distance the anterior region of the tibia plateau contourline 322 x needs to be restored.

As shown in FIGS. 44I and 44J, which are respectively the same views asFIGS. 44G and 44H, the damaged region 401 x of the of the tibia plateaucontour line 322 x is extended up to intersect the reference vector U₁,thereby restoring the missing posterior high point Q4′ in the case ofFIG. 44I and the anterior high point Q4 in the case of and FIG. 44J, therestoring resulting in restored regions 403 x. As can be understood fromFIGS. 44E, 44F, 44I and 44J, in one embodiment, the reference side femurcondyle ellipse 305 x-N1 may be applied to the damaged side tibiaplateau 306 x to serve as a guide to locate the proper offset distanceL4 between the existing high point (i.e., Q4 in FIG. 44I and Q4′ in FIG.44J) and the newly restored high point (i.e., Q4′ in FIG. 44I and Q4 inFIG. 44J) of the restored region 403 x. Also, in one embodiment, thereference side femur condyle ellipse 305 x-N1 may be applied to thedamaged side tibia plateau 306 x to serve as a guide to achieve theproper curvature for the tibia plateau contour line 322 x. The curvatureof the tibia plateau contour line 322 x may such that the contour line322 x near the midpoint between the anterior and posterior high pointsQ4, Q4′ is offset from the reference vector U₁ by a distance h₄. In someembodiments, the ratio of the distances h₄/L₄ after the restoration isless than approximately 0.01. As discussed above, the reference ellipsemay be applied to the damaged contour line and adjusted in size, butmaintaining the ratio, until the ellipse matches a portion of thedamaged contour line.

As discussed above with respect to the femur condyle image slices beingpositionally referenced to each other via a femur reference axis AO_(F),and as can be understood from FIG. 44B, each tibia image slice N1, N2,N3, N4 will be generated relative to a tibia reference axis AO_(T),which may be the same as or different from the femur reference axisAO_(F). The tibia reference axis AO_(T) will extend medial-lateral andmay pass through a center point of each area defined by the contour lineof each tibia image slice N1, N2, N3, N4. The tibia reference axisAO_(T) may extend through other regions of the tibia image slices N1,N2, N3, N4 or may extend outside of the tibia image slices, even, forexample, through the origins O₁, O₂, O₃, O₄ of the respective femurimages slices N1, N2, N3, N4 (in such a case the tibia reference axisAO_(F) and femur reference axis AO_(F) may be the same or share the samelocation).

The axis AO_(T) can be used to properly orient reference side data(e.g., the ellipse 305 x-N1 and vector U₁ of the N1 slice in the currentexample) when being superimposed onto a damaged side image slice (e.g.,the N4 image slice in the current example). The orientation of the dataor information of the reference side does not change as the data orinformation is being superimposed or otherwise applied to the damagedside image slice. For example, the orientation of the ellipse 305 x-N1and vector U₁ of the N1 slice is maintained or held constant during thesuperimposing of such reference information onto the N4 slice such thatthe reference information does not change when being superimposed on orotherwise applied to the N4 slice. Thus, since the reference sideinformation is indexed to the damaged side image slice via the axisAO_(T) and the orientation of the reference side information does notchange in the process of being applied to the damaged side image slice,the reference side information can simply be adjusted with respect tosize to assist in the restoration of the damaged side image slice.

The contour line N₄ of the N4 image slice, as with any contour line ofany femur or tibia image slice, may be generated via an open or closedloop computer analysis of the cortical bone of the medial tibia plateau306 x in the N4 image slice, thereby outlining the cortical bone with anopen or closed loop contour line N₄. Where the contour lines are closedloop, the resulting 3D models 22, 28 will be 3D volumetric models. Wherethe contour lines are open loop, the resulting 3D models 22, 28 will be3D surface models.

The preceding example discussed with respect to FIGS. 44A-44J is givenin the context of the lateral femur condyle 300 x serving as thereference side and the medial femur condyle 302 x and medial tibiacondyle 306 x being the damaged sides. Specifically, reference data orinformation (e.g., ellipses, vectors, etc.) from lateral femur condyle300 x is applied to the medial femur condyle 302 x and medial tibiaplateau 306 x for the restoration thereof. The restoration process forthe contour lines of the damaged side femur condyle 302 x and damagedside tibia plateau 306 x take place slice-by-slice for the image slices16 forming the damaged side of the bone models 22A, 22B ([block 220] ofFIG. 41). The restored image slices 16 are then utilized when a 3Dcomputer modeling program recompiles the image slices 16 to generate therestored bone models 28A, 28B ([block 225] of FIG. 41).

While a specific example is not given to illustrate the reversedsituation, wherein the medial femur condyle 302 x serves as thereference side and the lateral femur condyle 300 x and lateral tibiacondyle 304 x are the damaged sides, the methodology is the same asdiscussed with respect to FIGS. 44A-44J and need not be discussed insuch great detail. It is sufficient to know that reference data orinformation (e.g., ellipses, vectors, etc.) from the medial femurcondyle 302 x is applied to the lateral femur condyle 300 x and lateraltibia plateau 304 x for the restoration thereof, and the process is thesame as discussed with respect to FIGS. 44A-44J.

2. Employing Vectors from a Tibia Plateau of a Reference Side of a KneeJoint to Restore the Tibia Plateau of the Damaged Side

A damaged side tibia plateau can also be restored by applying data orinformation from the reference side tibia plateau to the damaged sidetibia plateau. In this example, the damaged side tibia plateau will bethe medial tibia plateau 306 x, and the reference side tibia plateauwill be the lateral tibia plateau 304 x.

In one embodiment, the process of restoring the damaged side tibiaplateau 306 x begins by analyzing the reference side tibia plateau 304 xto determine the highest anterior point and a highest posterior point ofthe reference side tibia plateau 304 x. Theses highest points can thenbe used to determine the reference vector.

In one embodiment, as can be understood from FIG. 43C as viewed alongthe N3 image slice, the reference side tibia plateau 304 x can beanalyzed via tangent lines to identify the highest points Q3, Q3′. Forexample, tangent line T_(Q3) can be used to identify the anteriorhighest point Q3, and tangent line T_(Q3)′ can be used to identify theposterior highest point Q3′. In some embodiments, a vector extendingbetween the highest points Q3, Q3′ may be generally perpendicular to thetangent lines T_(Q3), T_(Q3)′.

In another embodiment, the reference side femur condyle ellipse 305 x-N1can be applied to the reference side lateral tibia plateau 304 x todetermine the highest anterior or posterior points Q3, Q3′ along the N3image slice. For example, as can be understood from FIG. 43A, thereference side femur condyle ellipse 305 x-N1 (or ellipse 305 x-N3 ifanalyzed in the N3 image slice) can be applied to the reference sidelateral tibia plateau 304 x to identify the anterior highest point Q1 ofthe tibia plateau 304 x, and the reference side femur condyle ellipse305 x-N1 (or ellipse 305 x-N3 if analyzed in the N3 image slice) can beapplied to the reference side lateral tibia plateau 304 x to identifythe posterior highest point Q1′ of the tibia plateau 306 x. Where theellipse 305 x-N3 of the N3 image slice is utilized, the highest tibiaplateau points may be Q3, Q3′.

As can be understood from FIG. 43A, once the highest points aredetermined, a reference vector can be determined by extending a vectorthrough the points. For example, vector V₁ can be found by extending thevector through highest tibia plateau points Q1, Q1′ in the N1 slice.

In one embodiment, the process of restoring the damaged side tibiaplateau 306 x continues by analyzing the damaged side tibia plateau 306x to determine at least one of a highest anterior point or a highestposterior point of the damaged side tibia plateau 306 x.

In one embodiment, as can be understood from FIG. 43C as viewed alongthe N4 image slice and assuming the damage to the medial tibia plateau306 x is not so extensive that at least one of the highest anterior orposterior points Q4, Q4′ still exists, the damaged tibia plateau 306 xcan be analyzed via tangent lines to identify the surviving high pointQ4, Q4′. For example, if the damage to the medial tibia plateau 306 xwas concentrated in the posterior region such that the posterior highestpoint Q4′ no longer existed, the tangent line T_(Q4) could be used toidentify the anterior highest point Q4. Similarly, if the damage to themedial tibia plateau 306 x was concentrated in the anterior region suchthat the anterior highest point Q4 no longer existed, the tangent lineT_(Q4′) could be used to identify the posterior highest point Q4′.

In another embodiment, the reference side femur condyle ellipse 305 x-N1can be applied to the damaged medial tibia plateau 306 x to determine atleast one of the highest anterior or posterior points Q4, Q4′ along theN4 image slice. This process may be performed assuming the damage to themedial tibia plateau 306 x is not so extensive that at least one of thehighest anterior or posterior points Q4, Q4′ still exists. For example,as illustrated by FIG. 44E, which is a sagittal view of the medial tibiaplateau 306 x along the N4 image slice, wherein damage 401 x to theplateau 306 x is mainly in the posterior region, the reference sidefemur condyle ellipse 305 x-N1 can be applied to the damaged medialtibia plateau 306 x to identify the anterior highest point Q4 of thetibia plateau 306 x. Similarly, in another example, as illustrated byFIG. 44F, which is a sagittal view of the medial tibia plateau 306 xalong the N4 image slice, wherein damage 401 x to the plateau 306 x ismainly in the anterior region, the reference side femur condyle ellipse305 x-N1 can be applied to the damaged medial tibia plateau 306 x toidentify the posterior highest point Q4′ of the tibia plateau 306 x.

In one embodiment in a manner similar to that discussed above withrespect to FIGS. 44C2 and 44C3, the reference information (e.g., N1information such as the N1 ellipse) may be applied to the damagedcontour line via the AO axis and adjusted in size (e.g., made smaller orlarger) until the N1 ellipse matches a portion of the damaged contourline in order to find the highest point, which may be, for example, Q4or Q4′. As explained above with respect to FIGS. 44C2 and 44C3, theadjustments in size for reference information may be made whilemaintaining the ratio of the N1 information.

Once the highest point is determined through any of the above-describedmethods discussed with respect to FIGS. 43C, 44E and 44F, the referenceside tibia plateau vector can be applied to the damaged side tibiaplateau to determine the extent to which the tibia plateau contour line322 x needs to be restored ([block 215] of FIG. 41). For example, as canbe understood from FIGS. 44K and 44L, which are respectively the sameviews as FIGS. 44G and 44H, the vector from the reference side lateraltibia plateau 304 x (e.g., the vector V₁ from the N1 image slice) isapplied to the damaged side medial tibia plateau 306 x such that thevector V₁ intersects the existing highest point. Thus, as shown in FIG.44K, where the existing highest point is the anterior point Q4, thevector V₁ will extend through the anterior point Q4 and will spacedapart from damage 401 x in the posterior region of the tibia plateaucontour line 322 x by the distance the posterior region of the tibiaplateau contour line 322 x needs to be restored. Similarly, as shown inFIG. 44L, where the existing highest point is the posterior point Q4′,the vector V₁ will extend through the posterior point Q4′ and willspaced apart from the damage 401 x of the anterior region of the tibiaplateau contour line 322 x by the distance the anterior region of thetibia plateau contour line 322 x needs to be restored.

As shown in FIGS. 44M and 44N, which are respectively the same views asFIGS. 44I and 44J, the damaged region 401 x of the of the tibia plateaucontour line 322 x is extended up to intersect the reference vector V₁,thereby restoring the missing posterior high point Q4′ in the case ofFIG. 44M and the anterior high point Q4 in the case of and FIG. 44N, therestoring resulting in restored regions 403 x. As can be understood fromFIGS. 44E, 44F, 44M and 44N, in one embodiment, the reference side femurcondyle ellipse 305 x-N1 may be applied to the damaged side tibiaplateau 306 x to serve as a guide to locate the proper offset distanceL4 between the existing high point (i.e., Q4 in FIG. 44M and Q4′ in FIG.44N) and the newly restored high point (i.e., Q4′ in FIG. 44M and Q4 inFIG. 44N) of the restored region 403 x. Also, in one embodiment, thereference side femur condyle ellipse 305 x-N1 may be applied to thedamaged side tibia plateau 306 x to serve as a guide to achieve theproper curvature for the tibia plateau contour line 322 x. The curvatureof the tibia plateau contour line 322 x may such that the contour line322 x near the midpoint between the anterior and posterior high pointsQ4, Q4′ is offset from the reference vector U₁ by a distance h4. In someembodiments, the ratio of the distances h₄/L₄ after the restoration isless than approximately 0.01. As discussed above, the reference ellipsemay be applied to the damaged contour line and adjusted in size, butmaintaining the ratio, until the ellipse matches a portion of thedamaged contour line.

As discussed above with respect to the femur condyle image slices beingpositionally referenced to each other via a femur reference axis AO_(F),and as can be understood from FIG. 44B, each tibia image slice N1, N2,N3, N4 will be generated relative to a tibia reference axis AO_(T),which may be the same as or different from the femur reference axisAO_(F). The tibia reference axis AO_(T) will extend medial-lateral andmay pass through a center point of each area defined by the contour lineof each tibia image slice N1, N2, N3, N4. The tibia reference axisAO_(T) may extend through other regions of the tibia image slices N1,N2, N3, N4 or may extend outside of the tibia image slices, even, forexample, through the origins O₁, O₂, O₃, O₄ of the respective femurimages slices N1, N2, N3, N4 (in such a case the tibia reference axisAO_(F) and femur reference axis AO_(F) may be the same or share the samelocation).

The axis AO_(T) can be used to properly orient reference side data(e.g., the ellipse 305 x-N1 and vector V₁ of the N1 slice in the currentexample) when being superimposed onto a damaged side image slice (e.g.,the N4 image slice in the current example). The orientation of the dataor information of the reference side does not change as the data orinformation is being superimposed or otherwise applied to the damagedside image slice. For example, the orientation of the ellipse 305 x-N1and vector V₁ of the N1 slice is maintained or held constant during thesuperimposing of such reference information onto the N4 slice such thatthe reference information does not change when being superimposed on orotherwise applied to the N4 slice. Thus, since the reference sideinformation is indexed to the damaged side image slice via the axisAO_(T) and the orientation of the reference side information does notchange in the process of being applied to the damaged side image slice,the reference side information can simply be adjusted with respect tosize to assist in the restoration of the damaged side image slice.

The contour line N₄ of the N4 image slice, as with any contour line ofany femur or tibia image slice, may be generated via an open or closedloop computer analysis of the cortical bone of the medial tibia plateau306 x in the N4 image slice, thereby outlining the cortical bone with anopen or closed loop contour line N₄. Where the contour lines are closedloop, the resulting 3D models 22, 28 will be 3D volumetric models. Wherethe contour lines are open loop, the resulting 3D models 22, 28 will be3D surface models.

In the current example discussed with respect to FIGS. 44K-44N, theinformation from the reference side tibia plateau 304 x is employed torestore the damaged side tibia plateau 306 x. However, the informationfrom the reference side femur condyle 300 x is still used to restore thedamaged side femur condyle 302 x as discussed above in the precedingexample with respect to FIGS. 44A-44D.

The preceding example discussed with respect to FIGS. 44K-44N is givenin the context of the lateral tibia plateau 304 x and lateral femurcondyle 300 x serving as the reference sides and the medial femurcondyle 302 x and medial tibia condyle 306 x being the damaged sides.Specifically, reference data or information (e.g., vectors from thelateral tibia plateau 304 x and ellipses, vectors, etc. from the lateralfemur condyle 300 x) are applied to the medial femur condyle 302 x andmedial tibia plateau 306 x for the restoration thereof. The restorationprocess for the contour lines of the damaged side femur condyle 302 xand damaged side tibia plateau 306 x take place slice-by-slice for theimage slices 16 forming the damaged side of the bone models 22A, 22B([block 220] of FIG. 41). The restored image slices 16 are then utilizedwhen a 3D computer modeling program recompiles the image slices 16 togenerate the restored bone models 28A, 28B ([block 225] of FIG. 41).

While a specific example is not given to illustrate the reversedsituation, wherein the medial tibia plateau 306 x and medial femurcondyle 302 x serve as the reference sides and the lateral femur condyle300 x and lateral tibia condyle 304 x are the damaged sides, themethodology is the same as discussed with respect to FIGS. 44A-5D and44K-44N and need not be discussed in such great detail. It is sufficientto know that reference data or information (e.g., ellipses, vectors,etc.) from the medial tibia plateau 306 x and medial femur condyle 302 xare applied to the lateral femur condyle 300 x and lateral tibia plateau304 x for the restoration thereof, and the process is the same asdiscussed with respect to FIGS. 44A-44D and 44K-44N.

C. Verifying Accuracy of Restored Bone Model

Once the bone models 22A, 22B are restored into restored bone models28A, 28B as discussed in the preceding sections, the accuracy of thebone restoration process is checked ([block 230] of FIG. 41). Beforediscussion example methodology of conducting such accuracy checks, thefollowing discussion regarding the kinetics surround a knee joint isprovided.

The morphological shape of the distal femur and its relation to theproximal tibia and the patella suggests the kinetics of the knee (e.g.,see Eckhoff et al., “Three-Dimensional Mechanics, Kinetics, andMorphology of the Knee in Virtual Reality”, JBJS (2005); 87:71-80). Themovements that occur at the knee joint are flexion and extension, withsome slight amount of rotation in the bent position. During themovement, the points of contact of the femur with the tibia areconstantly changing. Thus, in the flexed position (90° knee extension),the hinder part of the articular surface of the tibia is in contact withthe rounded back part of the femoral condyles. In the semiflexedposition, the middle parts of the tibia facets articulate with theanterior rounded part of the femoral condyles. In the fully extendedposition (0° knee extension), the anterior and the middle parts of thetibia facets are in contact with the anterior flattened portion of thefemoral condyles.

With respect to the patella, in extreme flexion, the inner articularfacet rests on the outer part of the internal condyle of the femur. Inflexion, the upper part of facets rest on the lower part of thetrochlear surface of the femur. In mid-flexion, the middle pair rest onthe middle of the trochlear surface. However, in extension, the lowerpair of facets on the patella rest on the upper portion of the trochlearsurface of the femur. The difference may be described as the shifting ofthe points of contact of the articulate surface.

The traditional knee replacement studies focus mainly around thetibial-femoral joint. The methods disclosed herein employ the patella ina tri-compartmental joint study by locating the patella groove of theknee. The posterior surface of patella presents a smooth oval articulararea divided into two facets by a vertical ridge, the facets forming themedial and lateral parts of the same surface.

The vertical ridge of the posterior patella corresponds to the femoraltrochlear groove. In the knee flexion/extension motion movement, thepatella normally moves up and down in the femoral trochlear grove alongthe vertical ridge and generates quadriceps forces on the tibia. Thepatellofemoral joint and the movement of the femoral condyles play amajor role in the primary structure/mechanics across the joint. When theknee is moving and not fully extended, the femoral condyle surfaces bearvery high load or forces. In a normal knee, the patella vertical ridgeis properly aligned along the femoral trochlear groove so this alignmentprovides easy force generation in the sliding movement. If the patellais not properly aligned along the trochlear groove or tilted in certainangles, then it is hard to initiate the sliding movement so it causesdifficulty with respect to walking. Further, the misaligned axis alongthe trochlear groove can cause dislocation of the patella on thetrochlear groove, and uneven load damage on the patella as well.

The methods disclosed herein for the verification of the accuracy of thebone restoration process employ a “trochlear groove axis” or the“trochlear groove reference plane” as discussed below. This axis orreference plane extend across the lowest extremity of trochlear groovein both the fully-extended and 90° extension of the knee. Moreover, inrelation to the joint line, the trochlear groove axis is perpendicularor generally perpendicular to the joint line of the knee.

Because the vertical ridge of the posterior patella is generallystraight (vertical) in the sliding motion, the corresponding trochleargroove axis should be straight as well. The trochlear groove axis isapplied into the theory that the joint line of the knee is parallel tothe ground. In a properly aligned knee or normal knee, the trochleargroove axis is presumed to be perpendicular or nearly perpendicular tothe joint line.

For the OA, rarely is there bone damage in the trochlear groove,typically only cartilage damage. Thus, the femoral trochlear groove canserve as a reliable bone axis reference for the verification of theaccuracy of the bone restoration when restoring a bone model 22 into arestored bone model 28.

For a detailed discussion of the methods for verifying the accuracy ofthe bone restoration process, reference is made to FIGS. 45A-45D. FIG.45A is a sagittal view of a femur restored bone model 28A illustratingthe orders and orientations of imaging slices 16 (e.g., MRI slices, CTslices, etc.) forming the femur restored bone model 28A. FIG. 45B is thedistal images slices 1-5 taken along section lines 1-5 of the femurrestored bone model 28A in FIG. 45A. FIG. 45C is the coronal imagesslices 6-8 taken along section lines 6-8 of the femur restored bonemodel 28A in FIG. 45A. FIG. 45D is a perspective view of the distal endof the femur restored bone model 28A.

As shown in FIG. 45A, a multitude of image slices are compiled into thefemur restored bone model 28A from the image slices originally formingthe femur bone model 22A and those restored image slices modified viathe above-described methods. Image slices may extend medial-lateral inplanes that would be normal to the longitudinal axis of the femur, suchas image slices 1-5. Image slices may extend medial-lateral in planesthat would be parallel to the longitudinal axis of the femur, such asimage slices 6-8. The number of image slices may vary from 1-50 and maybe spaced apart in a 2 mm spacing.

As shown in FIG. 45B, each of the slices 1-5 can be aligned verticallyalong the trochlear groove, wherein points G1, G2, G3, G4, G5respectively represent the lowest extremity of trochlear groove for eachslice 1-5. By connecting the various points G1, G2, G3, G4, G5, a pointO can be obtained. As can be understood from FIGS. 42B and 45D,resulting line GO is perpendicular or nearly perpendicular to tangentline P₁P₂. In a 90° knee extension in FIG. 42B, line GO is perpendicularor nearly perpendicular to the joint line of the knee and line P₁P₂.

As shown in FIG. 45C, each of the slices 6-8 can be aligned verticallyalong the trochlear groove, wherein points H6, H7, H8 respectivelyrepresent the lowest extremity of the trochlear groove for each slice6-8. By connecting the various points H6, H7, H8, the point O can againbe obtained. As can be understood from FIGS. 42A and 45D, resulting lineHO is perpendicular or nearly perpendicular to tangent line D₁D₂. In a0° knee extension in FIG. 42A, line HO is perpendicular or nearlyperpendicular to the joint line of the knee and line D₁D₂.

As illustrated in FIG. 45D, the verification of the accuracy of therestoration process includes determining if the reference lines GO andHO are within certain tolerances with respect to being parallel tocertain lines and perpendicular to certain lines. The line GO, as thereference across the most distal extremity of the trochlear groove ofthe femur and in a 90° knee extension, should be perpendicular totangent line D₁D₂. The line HO, as the reference across the mostposterior extremity of trochlear groove of the femur and in a 0° kneeextension, should be perpendicular to tangent line P₁P₂.

Line HO and line P₁P₂ may form a plane S, and lines GO and line D₁ D₂may form a plane P that is perpendicular to plane S and forms line SRtherewith. Line HO and line GO are parallel or nearly parallel to eachother. Lines P₁P₂, D₁ D₂ and SR are parallel or nearly parallel to eachother. Lines P₁P₂, D₁D₂ and SR are perpendicular or nearly perpendicularto lines HO and GO.

As can be understood from FIG. 45D, in one embodiment, lines HO and GOmust be within approximately three degrees of being perpendicular withlines P₁P₂, and D₁D₂ or the restored bones models 28A, 28B will berejected and the restoration process will have to be repeated until theresulting restored bone models 28A, 28B meet the stated tolerances, orthere has been multiple failed attempts to meet the tolerances ([block230]-[block 240] of FIG. 41). Alternatively, as can be understood fromFIG. 45D, in another embodiment, lines HO and GO must be withinapproximately six degrees of being perpendicular with lines P₁P₂, andD₁D₂ or the restored bones models 28A, 28B will be rejected and therestoration process will have to be repeated until the resultingrestored bone models 28A, 28B meet the stated tolerances, or there hasbeen multiple failed attempts to meet the tolerances ([block 230]-[block240] of FIG. 41). If multiple attempts to provide restored bone models28A, 28B satisfying the tolerances have been made without success, thenbone restoration reference data may be obtained from another similarjoint that is sufficiently free of deterioration. For example, in thecontext of knees, if repeated attempts have been made without success torestore a right knee medial femur condyle and tibia plateau fromreference information obtained from the right knee lateral sides, thenreference data could be obtained from the left knee lateral or medialsides for use in the restoration process in a manner similar todescribed above.

In some embodiments, as depicted in the table illustrated in FIG. 46,some OA knee conditions are more likely to be restored via the methodsdisclosed herein than other conditions when it comes to obtaining thereference data from the same knee as being restored via the referencedata. For example, the damaged side of the knee may be light (e.g., nobone damage or bone damage less than 1 mm), medium (e.g., bone damage ofapproximately 1 mm) or severe (e.g., bone damage of greater than 1 mm).As can be understood from FIG. 46, the bone restoration provided viasome of the above-described embodiments may apply to most OA patientshaving light-damaged knees and medium-damaged knees and some OA patientshaving severe-damaged knees, wherein restoration data is obtained from areference side of the knee having the damaged side to be restored.However, for most OA patients having severe-damaged and some OA patientshaving medium-damaged knees, in some embodiments as described below,bone restoration analysis entails obtaining restoration data from a goodfirst knee of the patient for application to, and restoration of, a badsecond knee of the patient.

It should be understood that the indications represented in the table ofFIG. 46 are generalities for some embodiments disclosed herein withrespect to some patients and should not be considered as absoluteindications of success or failure with respect to whether or not any oneor more of the embodiments disclosed herein may be successfully appliedto an individual patient having any one of the conditions (light,medium, severe) reflected in the table of FIG. 46. Therefore, the tableof FIG. 46 should not be considered to limit any of the embodimentsdisclose herein.

D. Further Discussion of Bone Model Restoration Methods

For further discussion regarding embodiments of bone model restorationmethods, reference is made to FIGS. 47A-47D. FIG. 47A shows theconstruction of reference line SQ in a medial portion of the tibiaplateau. In one embodiment, the reference line SQ may be determined bysuperimposing an undamaged femoral condyle ellipse onto the medial tibiaplateau to obtain two tangent points Q and S. In another embodiment, thetangent points Q and S may be located from the image slices byidentifying the highest points at the posterior and anterior edges ofthe medial tibia plateau. By identifying tangent points Q and S, thetangent lines QP and SR may be determined by extending lines across eachof the tangent points Q and S, wherein the tangent lines QP and SR arerespectively tangent to the anterior and posterior curves of the medialtibia plateau. Reference line SQ may be obtained where tangent line QPis perpendicular or generally perpendicular to reference line SQ andtangent line SR is perpendicular or generally perpendicular to referenceline SQ.

FIG. 47B shows the restoration of a damaged anterior portion of thelateral tibia plateau. The reference vector line or the vector plane isobtained from FIG. 47A, as line SQ or plane SQ. The reference vectorplane SQ from the medial side may be applied as the reference plane inthe damaged lateral side of the tibia plateau surface. In FIG. 47B, thecontour of the damaged anterior portion of the lateral tibia plateau maybe adjusted to touch the proximity of the reference vector plane SQ fromthe undamaged medial side. That is, points S′ and Q′ are adjusted toreach the proximity of the plane SQ. The outline between points S′ andQ′ are adjusted and raised to the reference plane SQ. By doing thisadjustment, a restored tangent point Q′ may be obtained via this vectorplane SQ reference.

As shown in FIG. 47D, the reference vector plane SQ in the medial sideis parallel or nearly parallel to the restored vector plane S′Q′ in thelateral side. In FIG. 47B, the length L″ represents the length of lineS′Q′. The length l″ is the offset from the recessed surface region ofthe tibia plateau to the plane S′Q′ after the restoration. In the bonerestoration assessment, the ratio of l″IL″ may be controlled to be lessthan 0.01.

FIG. 47C is the coronal view of the restored tibia after 3Dreconstruction, with a 0° knee extension model. The points U and Vrepresent the lowest extremity of tangent contact points on each of thelateral and medial tibia plateau, respectively. In one embodiment,tangent points U and V are located within the region between the tibiaspine and the medial and lateral epicondyle edges of the tibia plateau,where the slopes of tangent lines in this region are steady andconstant. In one embodiment, the tangent point U in the lateral plateauis in area I between the lateral side of lateral intercondylar tuberculeto the attachment of the lateral collateral ligament. For the medialportion, the tangent point V is in area II between the medial side ofmedial intercondylar tubercule to the medial condyle of tibia, as shownin FIG. 47C.

As previously stated, FIG. 47C represents the restored tibia models and,therefore, the reference lines N1 and N2 can apply to the restored tibiamodel in FIG. 47C, when the knee is at 0° extension. As can beunderstood from FIG. 47C, line N1 when extended across point U isperpendicular or generally perpendicular to line-UV, while line N2 whenextended across point V is perpendicular or generally perpendicular toline UV. In restored the tibia model, line UV may be parallel or nearlyparallel to the joint line of the knee. Within all these referencelines, in one embodiment, the tolerable range of the acute angle betweennearly perpendicular or nearly parallel lines or planes may be within anabsolute 6-degree angle, |X−X′|<6°. If the acute angle difference fromFIG. 47C is less than 6°, the numerical data for the femur and/or tibiarestoration is acceptable. This data may be transferred to the furtherassess the varus/valgus alignment of the knee models.

FIG. 48A is a coronal view of the restored knee models of proximal femurand distal tibia with 0° extension of the knee. Line ab extends acrossthe lowest extremity of trochlear groove of the distal femur model.Reference lines N1 and N2 are applied to the restored knee model ofvarus/valgus alignment, where line-N1 is parallel or generally parallelto line N2 and line ab. Depending on the embodiment, the acute anglesbetween these lines may be controlled within a 3 degree range or a 5degree range. The tangent points D and E represent the lowestextremities of the restored proximal femur model. The tangent points Uand V are obtained from the restored distal tibia plateau surface. Inthe medial portion, t1′ represents the offset of the tangent linesbetween the medial condyle and medial tibia plateau. In the lateralportion, t2′ represents the offset of the tangent lines between thelateral condyle and lateral tibia plateau. In the varus/valgus rotationand alignment, t1′ is substantially equal to t2′, or |t1′−t2′|<<1 mm.Therefore, line DE may be generally parallel to the joint line of theknee and generally parallel to line UV.

FIG. 48B is a sagittal view of the restored knee models. Line 348represents the attachment location of lateral collateral ligament whichlies on the lateral side of the joint. Line 342 represents the posteriorextremity portion of the lateral femoral condyle. Line 344 representsthe distal extremity portion of the lateral condyle. In this restoredknee model, line 344 may be parallel or generally parallel to line L.That is, plane 344 is parallel or generally parallel to plane L andparallel or generally parallel to the joint plane of the knee. In oneembodiment, the tolerable range of acute angle between these planes maybe controlled within an absolute 6 degrees. If the angle is less than anabsolute 6 degrees, the information of the femur and tibia model willthen be forwarded to the preoperative design for the implant modeling.If the acute angle is equal or larger than an absolute 6 degrees, theimages and 3D models will be rejected. In this situation, the procedurewill be returned to start all over from the assessment procedure ofreference lines/planes.

E. Using Reference Information from a Good Joint to Create a RestoredBone Model for a Damaged Joint

As mentioned above with respect to the table of FIG. 46, the knee thatis the target of the arthroplasty procedure may be sufficiently damagedon both the medial and lateral sides such that neither side mayadequately serve as a reference side for the restoration of the otherside. In a first embodiment and in a manner similar to that discussedabove with respect to FIGS. 41-45D, reference data for the restorationof the deteriorated side of the target knee may be obtained from thepatient's other knee, which is often a healthy knee or at least has ahealthy side from which to obtain reference information. In a secondembodiment, the image slices of the healthy knee are reversed in amirrored orientation and compiled into a restored bone modelrepresentative of the deteriorated knee prior to deterioration, assumingthe patient's two knees where generally mirror images of each other whenthey were both healthy. These two embodiments, which are discussed belowin greater detail, may be employed when the knee targeted forarthroplasty is sufficiently damaged to preclude restoration in a mannersimilar to that described above with respect to FIGS. 41-45D. However,it should be noted that the two embodiments discussed below may also beused in place of, or in addition to, the methods discussed above withrespect to FIGS. 41-45D, even if the knee targeted for arthroplasty hasa side that is sufficiently healthy to allow the methods discussed abovewith respect to FIGS. 41-45D to be employed.

For a discussion of the two embodiments for creating a restored bonemodel for a deteriorated knee targeted for arthroplasty from imageslices obtained from a healthy knee, reference is made to FIGS. 49A and49B. FIG. 49A is a diagram illustrating the condition of a patient'sright knee, which is in a deteriorated state, and left knee, which isgenerally healthy. FIG. 49B is a diagram illustrating the twoembodiments. While in FIGS. 49A and 49B and the following discussion theright knee 702 x of the patient 700 x is designated as the deteriorateknee 702 x and the left knee 704 x of the patient 700 x is designated asthe healthy knee 704 x, of course such designations are for examplepurposes only and the conditions of the knees could be the reverse.

As indicated in FIG. 49A, the patient 700 x has a deteriorated rightknee 702 x formed of a femur 703 x and a tibia 707 x and which has oneor both of sides in a deteriorated condition. In this example, thelateral side 705 x of the right knee 702 x is generally healthy and themedial side 706 x of the right knee 702 x is deteriorated such that theright medial condyle 708 x and right medial tibia plateau 710 x willneed to be restored in any resulting restored bone model 28. As can beunderstood from FIG. 49A, the patient also has a left knee 704 x that isalso formed of a femur 711 x and a tibia 712 x and which has a medialside 713 x and a lateral side 714 x. In FIG. 49A, both sides 713 x, 714x of the left knee 704 x are generally healthy, although, for one of thefollowing embodiments, a single healthy side is sufficient to generate arestored bone model 28 for the right knee 702 x.

As indicated in FIG. 49B, image slices 16 of the deteriorated right knee702 x and healthy left knee 704 x are generated as discussed above withrespect to FIGS. 1A and 1B. In the first embodiment, which is similar tothe process discussed above with respect to FIGS. 41-45D, except theprocess takes place with a deteriorated knee and a health knee asopposed to the deteriorated and healthy sides of the same knee,reference information (e.g., vectors, lines, planes, ellipses, etc. asdiscussed with respect to FIGS. 41-45D) 720 x is obtained from a healthyside of the healthy left knee 704 x [block 1000 of FIG. 49B]. Thereference information 720 x obtained from the image slices 16 of thehealth left knee 704 x is applied to the deteriorated sides of the rightknee 702 x [block 1005 of FIG. 49B]. Specifically, the applied referenceinformation 720 x is used to modify the contour lines of the imagesslices 16 of the deteriorated sides of the right knee 702 x, after whichthe modified contour lines are compiled, resulting in a restored bonemodel 28 that may be employed as described with respect to FIG. 1C. Thereference information 720 x obtained from the healthy left knee imageslices 16 may be coordinated with respect to position and orientationwith the contour lines of the deteriorated right knee image slices 16 byidentifying a similar location or feature on each knee joint that isgenerally identical between the knees and free of bone deterioration,such as a point or axis of the femur trochlear groove or tibia plateauspine.

In the second embodiment, image slices 16 are generated of both thedeteriorate right knee 702 x and healthy left knee 704 x as discussedabove with respect to FIG. 1B. The image slices 16 of the deterioratedright knee 702 x may be used to generate the arthritic model 36 asdiscussed above with respect to FIG. 1D. The image slices 16 of thehealthy left knee 704 x are mirrored medially/laterally to reverse theorder of the image slices 16 [block 2000 of FIG. 49B]. Themirrored/reversed order image slices 16 of the healthy left knee 704 xare compiled, resulting in a restored bone model 28 for the right knee702 x that is formed from the image slices 16 of the left knee 704 x[block 2000 of FIG. 49B]. In other words, as can be understood from[block 2000] and its associated pictures in FIG. 49B, bymedially/laterally mirroring the image slices 16 of left knee 704 x tomedially/laterally reverse their order and then compiling them in such areversed order, the image slices 16 of the left knee 704 x may be formedinto a bone model that would appear to be a bone model of the right knee702 x in a restored condition, assuming the right and left knees 702 x,704 x were generally symmetrically identical mirror images of each otherwhen both were in a non-deteriorated state.

To allow for the merger of information (e.g., saw cut and drill holedata 44 and jig data 46) determined respectively from the restored bonemodel 28 and the arthritic model 28 as discussed above with respect toFIG. 1E, the restored bone model 28 generated from the mirrored imageslices 16 of the healthy left knee 704 x may be coordinated with respectto position and orientation with the arthritic model 36 generated fromthe image slices 16 of the deteriorated right knee 702 x. In oneembodiment, this coordination between the models 28, 36 may be achievedby identifying a similar location or feature on each knee joint that isgenerally identical between the knees and free of bone deterioration,such as a point or axis of the femur trochlear groove or tibia plateauspine. Such a point may serve as the coordination or reference point P′(X_(0-k), Y_(0-k), Z_(0-k)) as discussed with respect to FIG. 1E.

While the two immediately preceding embodiments are discussed in thecontext of knee joints, these embodiments, like the rest of theembodiments disclosed throughout this Detailed Description, are readilyapplicable to other types of joints including ankle joints, hip joints,wrist joints, elbow joints, shoulder joints, finger joints, toe joints,etc., and vertebrae/vertebrae interfaces and vertebrae/skull interfaces.Consequently, the content of this Detailed Description should not beinterpreted as being limited to knees, but should be consider toencompass all types of joints and bone interfaces, without limitation.

IV. Overview of Pre-Operative Surgical Planning Process

Section II. of the present disclosure describes the acquisition ofmedical images, the segmentation or auto-segmentation of the medicalimages, and the generation of a patient bone model from the segmentedimages that is representative of the bones of the patient in adeteriorated or degenerated state. Section III. of the presentdisclosure describes exemplary methods of modifying image data (e.g., 2Dimage slices) of a patient's bone in a deteriorated state to restoredimage data (e.g., restored 2D image slices) that may be used to generatea restored bone model representing the patient's bone in apre-deteriorated or pre-degenerated state. Beginning in Section IV., thepresent disclosure describes exemplary methods of implant planning(e.g., determining coordinate locations for resections, implant sizes)utilizing the bone models or image data (e.g., 2D image slices, restored2D image slices) described previously. As described herein, the implantplanning may take place utilizing the image data (e.g., 2D image slices)of the bone models representative of the patient's bones in apre-deteriorated state (described in Section III) or a deterioratedstate (described in Section II).

Disclosed herein are customized arthroplasty jigs 2 and systems 4 for,and methods of, producing such jigs 2. The jigs 2 are customized to fitspecific bone surfaces of specific patients. Depending on theembodiment, the jigs 2 are automatically planned and generated and maybe similar to those disclosed in these three U.S. patent applications:U.S. patent application Ser. No. 11/656,323 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Jan. 19, 2007, nowU.S. Pat. No. 9,017,336; U.S. patent application Ser. No. 10/146,862 toPark et al., titled “Improved Total Joint Arthroplasty System” and filedMay 15, 2002; and U.S. patent Ser. No. 11/642,385 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006. Thedisclosures of these three U.S. patent applications are incorporated byreference in their entireties into this Detailed Description.

A. Overview of System and Method for Manufacturing CustomizedArthroplasty Cutting Jigs

For an overview discussion of the systems 4 for, and methods of,producing the customized arthroplasty jigs 2, reference is made to FIGS.1A-1I AND 50A-50E. FIG. 1A is a schematic diagram of a system 4 foremploying the automated jig production method disclosed herein. FIGS.50A-50E are flow chart diagrams outlining the jig production methoddisclosed herein. The following overview discussion can be broken downinto three sections.

The first section, which is discussed with respect to FIG. 1A and[blocks 100-125] of FIGS. 50A, 50B, 50C, and 50E, pertains to an examplemethod of determining, in a two-dimensional (“2D”) computer modelenvironment, saw cut and drill hole locations 30, 32 relative to 2Dimages 16 of a patient's joint 14. The resulting “saw cut and drill holedata” 44 is planned to provide saw cuts 30 and drill holes 32 that willallow arthroplasty implants to restore the patient's joint to itspre-degenerated or natural alignment state.

The second section, which is discussed with respect to FIG. 1A and[blocks 100-105 and 130-145] of FIGS. 50A, 50D, and 50E, pertains to anexample method of importing into 3D computer generated jig models 38 3Dcomputer generated surface models 40 of arthroplasty target areas 42 of3D computer generated arthritic models 36 of the patient's joint bones.The resulting “jig data” 46 is used to produce a jig customized tomatingly receive the arthroplasty target areas of the respective bonesof the patient's joint.

The third section, which is discussed with respect to FIG. 1A and[blocks 150-165] of FIG. 50E, pertains to a method of combining orintegrating the “saw cut and drill hole data” 44 with the “jig data” 46to result in “integrated jig data” 48. The “integrated jig data” 48 isprovided to the CNC machine 10 or other rapid production machine (e.g.,a stereolithography apparatus (“SLA”) machine) for the production ofcustomized arthroplasty jigs 2 from jig blanks 50 provided to the CNCmachine 10. The resulting customized arthroplasty jigs 2 include saw cutslots and drill holes positioned in the jigs 2 such that when the jigs 2matingly receive the arthroplasty target areas of the patient's bones,the cut slots and drill holes facilitate preparing the arthroplastytarget areas in a manner that allows the arthroplasty joint implants togenerally restore the patient's joint line to its pre-degenerated stateor natural alignment state.

As shown in FIG. 1A, the system 4 includes a computer 6 having a CPU 7,a monitor or screen 9 and an operator interface controls 11. Thecomputer 6 is linked to a medical imaging system 8, such as a CT or MRImachine 8, and a computer controlled machining system 10, such as a CNCmilling machine 10.

As indicated in FIG. 1A, a patient 12 has a joint 14 (e.g., a knee,elbow, ankle, wrist, hip, shoulder, skull/vertebrae orvertebrae/vertebrae interface, etc.) to be replaced. The patient 12 hasthe joint 14 scanned in the imaging machine 8. The imaging machine 8makes a plurality of scans of the joint 14, wherein each scan pertainsto a thin slice of the joint 14.

As can be understood from FIG. 50A, the plurality of scans is used togenerate a plurality of two-dimensional (“2D”) images 16 of the joint 14[block 100 z]. Where, for example, the joint 14 is a knee 14, the 2Dimages will be of the femur 18 and tibia 20. The imaging may beperformed via CT or MRI. In one embodiment employing MRI, the imagingprocess may be as disclosed in U.S. patent application Ser. No.11/946,002 to Park, which is entitled “Generating MRI Images Usable ForThe Creation Of 3D Bone Models Employed To Make Customized ArthroplastyJigs,” was filed Nov. 27, 2007 and is incorporated by reference in itsentirety into this Detailed Description. The images 16 may be a varietyof orientations, including, for example, sagittal 2D images, coronal 2Dimages and axial 2D images.

As can be understood from FIG. 1A, the 2D images are sent to thecomputer 6 for analysis and for creating computer generated 2D modelsand 3D models. In one embodiment, the bone surface contour lines of thebones 18, 20 depicted in the image slices 16 may be auto segmented viaan image segmentation process as disclosed in U.S. Patent Application61/126,102, which was filed Apr. 30, 2008, is entitled System and Methodfor Image Segmentation in Generating Computer Models of a Joint toUndergo Arthroplasty, and is hereby incorporated by reference into thepresent application in its entirety.

As indicated in FIG. 50A, in one embodiment, reference point W isidentified in the 2D images 16 [block 105]. In one embodiment, asindicated in [block 105] of FIG. 1A, reference point W may be at theapproximate medial-lateral and anterior-posterior center of thepatient's joint 14. In other embodiments, reference point W may be atany other location in the 2D images 16, including anywhere on, near oraway from the bones 18, 20 or the joint 14 formed by the bones 18, 20.Reference point W may be defined at coordinates (X0-j, Y0-j, Z0-j)relative to an origin (X0, Y0, Z0) of an X-Y-Z axis and depicted inFIGS. 50A-50D as W (X0-j, Y0-j, Z0-j). Throughout the processesdescribed herein, to allow for correlation between the different typesof images, models or any other data created from the images, movementsof such images, models or any other data created form the images may betracked and correlated relative to the origin.

As described later in this overview, point W may be used to locate the2D images 16 and computer generated 3D model 36 created from the 2Dimages 16 respectively with the implant images 34 and jig blank model 38and to integrate information generated via the POP process. Depending onthe embodiment, point W, which serves as a position and/or orientationreference, may be a single point, two points, three points, a point plusa plane, a vector, etc., so long as the reference point W can be used toposition and/or orient the 2D images 16, 34 and 3D models 36, 38relative to each other as needed during the POP process.

As shown in FIG. 50B, the coronal and axial 2D images 16 of the femur 18forming the patient's joint 14 are analyzed to determine femur referencedata [block 110]. For example, the coronal 2D images are analyzed todetermine the most distal femur point D1 on a healthy condyle and ajoint line perpendicular to a trochlear groove line is used to estimatethe location of a hypothetical most distal point D2 on the damagedcondyle. Similarly, the axial 2D images are analyzed to determine themost posterior femur point P1 on a healthy condyle and a joint lineperpendicular to a trochlear groove line is used to estimate thelocation of a hypothetical most posterior point P2 on the damagedcondyle. The femur reference data points D1, D2, P1, P2 is mapped orotherwise imported to a sagittal or y-z plane in a computer environmentand used to determine the sagittal or y-z plane relationship between thefemur reference data points D1, D2, P1, P2. The femur reference data D1,D2, P1, P2 is then used to choose candidate femoral implant(s). [Block112]. The femur reference data points D1, D2, P1, P2 are respectivelycorrelated with similar reference data points D1′, D2′, P1′, P2′ of theselected femur implant 34 in a sagittal or y-z plane [block 114]. Thiscorrelation determines the locations and orientations of the cut plane30 and drill holes 32 needed to cause the patient's joint to returned toa natural, pre-deteriorated alignment with the selected implant 34. Thecut plane 30 and drill hole 32 locations determined in block 114 areadjusted to account for cartilage thickness [block 118].

As shown in FIG. 50C at block 120, tibia reference data is determinedfrom the images in a manner similar to the process of block 110, exceptdifferent image planes are employed. Specifically, sagittal and coronalimages slices of the tibia are analyzed to identify the lowest (i.e.,most distal) and most anterior and posterior points of the tibiarecessed condylar surfaces. This tibia reference data is then projectedonto an axial view. The tibia reference data is used to select anappropriate tibia implant [Block 121]. The tibia reference data iscorrelated to similar reference data of the selected tibia implant in amanner similar to that of block 114, except the correlation takes placein an axial view [Block 122]. The cut plane 30 associated with the tibiaimplant's position determined according to block 122 is adjusted toaccount for cartilage thickness [Block 123].

Once the saw cut locations 30 and drill hole locations 32 associatedwith the POP of the femur and tibia implants 34 has been completed withrespect to the femur and tibia data 28 (e.g., the 2D femur and tibiaimages 16 and reference point W), the saw cut locations 30 and drillhole locations 32 are packaged relative to the reference point W(X0-j,Y0-j, Z0-j) [Block 124]. As the images 16 and other data created fromthe images or by employing the images may have moved during any of theprocesses discussed in blocks 110-123, the reference point W(X0-j, Y0-j,Z0-j) for the images or associated data may become updated referencepoint W′ at coordinates (X0-k, Y0-k, Z0-k) relative to an origin (X0,Y0, Z0) of an X-Y-Z axis. For example, during the correlation processdiscussed in blocks 114 and 122, the implant reference data may be movedtowards the bone image reference data or, alternatively, the bone imagereference data may be moved towards the implant reference data. In thelatter case, the location of the bone reference data will move fromreference point W(X0-j, Y0-j, Z0-j) to updated reference point W′(X0-k,Y0-k, Z0-k), and this change in location with respect to the origin willneed to be matched by the arthritic models 36 to allow for “saw cut anddrill hole” data 44 obtained via the POP process of blocks 110-125 to bemerged with “jig data” 46 obtained via the jig mating surface definingprocess of blocks 130-145, as discussed below.

As can be understood from FIG. 50E, the POP process may be completedwith the packaging of the saw cut locations 30 and drill hole locations32 with respect to the updated reference point W′(X0-k, Y0-k, Z0-k) as“saw cut and drill hole data” 44 [Block 125]. The “saw cut and drillhole data” 44 is then used as discussed below with respect to [block150] in FIG. 50E.

In one embodiment, the POP procedure is a manual process, wherein 2Dbone images 28 (e.g., femur and tibia 2D images in the context of thejoint being a knee) are manually analyzed to determine reference data toaid in the selection of a respective implant 34 and to determine theproper placement and orientation of saw cuts and drill holes that willallow the selected implant to restore the patient's joint to itsnatural, pre-deteriorated state. (The reference data for the 2D boneimages 28 may be manually calculated or calculated by a computer by aperson sitting in front of a computer 6 and visually observing theimages 28 on the computer screen 9 and determining the reference datavia the computer controls 11. The data may then be stored and utilizedto determine the candidate implants and proper location and orientationof the saw cuts and drill holes. In other embodiments, the POP procedureis totally computer automated or a combination of computer automationand manual operation via a person sitting in front of the computer.

In some embodiments, once the selection and placement of the implant hasbeen achieved via the 2D POP processes described in blocks 110-125, theimplant selection and placement may be verified in 2D by superimposingthe implant models 34 over the bone images data, or vice versa.Alternatively, once the selection and placement of the implant has beenachieved via the 2D POP processes described in blocks 110-125, theimplant selection and placement may be verified in 3D by superimposingthe implant models 34 over 3D bone models generated from the images 16.Such bone models may be representative of how the respective bones mayhave appeared prior to degeneration. In superimposing the implants andbones, the joint surfaces of the implant models can be aligned or causedto correspond with the joint surfaces of the 3D bone models. This endsthe overview of the POP process. A more detailed discussion of variousembodiments of the POP process is provided later in this DetailedDescription

As can be understood from FIG. 50D, the 2D images 16 employed in the 2DPOP analysis of blocks 110-124 of FIGS. 50B-50C are also used to createcomputer generated 3D bone and cartilage models (i.e., “arthriticmodels”) 36 of the bones 18, 20 forming the patient's joint 14 [block130]. Like the above-discussed 2D images and femur and tibia referencedata, the arthritic models 36 are located such that point W is atcoordinates (X0-j, Y0-j, Z0-j) relative to the origin (X0, Y0, Z0) ofthe X-Y-Z axis [block 130]. Thus, the 2D images and femur and tibia dataof blocks 110-125 and arthritic models 36 share the same location andorientation relative to the origin (X0, Y0, Z0). Thisposition/orientation relationship is generally maintained throughout theprocess discussed with respect to FIGS. 50A-50E. Accordingly, movementsrelative to the origin (X0, Y0, Z0) of the 2D images and femur and tibiadata of blocks 110-125 and the various descendants thereof (i.e., bonecut locations 30 and drill hole locations 32) are also applied to thearthritic models 36 and the various descendants thereof (i.e., the jigmodels 38). Maintaining the position/orientation relationship betweenthe 2D images and femur and tibia data of blocks 110-125 and arthriticmodels 36 and their respective descendants allows the “saw cut and drillhole data” 44 to be integrated into the “jig data” 46 to form the“integrated jig data” 48 employed by the CNC machine 10 to manufacturethe customized arthroplasty jigs 2, as discussed with respect to block150 of FIG. 50E.

Computer programs for creating the 3D computer generated arthriticmodels 36 from the 2D images 16 include: Analyze from AnalyzeDirect,Inc., Overland Park, Kans.; Insight Toolkit, an open-source softwareavailable from the National Library of Medicine Insight Segmentation andRegistration Toolkit (“ITK”), www.itk.org; 3D Slicer, an open-sourcesoftware available from www.slicer.org; Mimics from Materialise, AnnArbor, Mich.; and Paraview available at www.paraview.org.

The arthritic models 36 depict the bones 18, 20 in the presentdeteriorated condition with their respective degenerated joint surfaces24, 26, which may be a result of osteoarthritis, injury, a combinationthereof, etc. The arthritic models 36 also include cartilage in additionto bone. Accordingly, the arthritic models 36 depict the arthroplastytarget areas 42 generally as they will exist when the customizedarthroplasty jigs 2 matingly receive the arthroplasty target areas 42during the arthroplasty surgical procedure.

As indicated in FIG. 50D and already mentioned above, to coordinate thepositions/orientations of the 2D images and femur and tibia data ofblocks 110-125 and arthritic models 36 and their respective descendants,any movement of the 2D images and femur and tibia data of blocks 110-125from point W to point W′ is tracked to cause a generally identicaldisplacement for the “arthritic models” 36, and vice versa [block 135].

As depicted in FIG. 50D, computer generated 3D surface models 40 of thearthroplasty target areas 42 of the arthritic models 36 are importedinto computer generated 3D arthroplasty jig models 38 [block 140]. Thus,the jig models 38 are configured or indexed to matingly receive thearthroplasty target areas 42 of the arthritic models 36. Jigs 2manufactured to match such jig models 38 will then matingly receive thearthroplasty target areas of the actual joint bones during thearthroplasty surgical procedure.

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is a manual process. The 3D computergenerated models 36, 38 are manually manipulated relative to each otherby a person sitting in front of a computer 6 and visually observing thejig models 38 and arthritic models 36 on the computer screen 9 andmanipulating the models 36, 38 by interacting with the computer controls11. In one embodiment, by superimposing the jig models 38 (e.g., femurand tibia arthroplasty jigs in the context of the joint being a knee)over the arthroplasty target areas 42 of the arthritic models 36, orvice versa, the surface models 40 of the arthroplasty target areas 42can be imported into the jig models 38, resulting in jig models 38indexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. Point W′ (X0-k, Y0-k, Z0-k) can also be importedinto the jig models 38, resulting in jig models 38 positioned andoriented relative to point W′ (X0-k, Y0-k, Z0-k) to allow theirintegration with the bone cut and drill hole data 44 of [block 125].

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is generally or completely automated, asdisclosed in U.S. patent application Ser. No. 11/959,344 to Park, whichis entitled System and Method for Manufacturing Arthroplasty Jigs, wasfiled Dec. 18, 2007, now U.S. Pat. No. 8,221,430 and is incorporated byreference in its entirety into this Detailed Description. For example, acomputer program may create 3D computer generated surface models 40 ofthe arthroplasty target areas 42 of the arthritic models 36. Thecomputer program may then import the surface models 40 and point W′(X0-k, Y0-k, Z0-k) into the jig models 38, resulting in the jig models38 being indexed to matingly receive the arthroplasty target areas 42 ofthe arthritic models 36. The resulting jig models 38 are also positionedand oriented relative to point W′ (X0-k, Y0-k, Z0-k) to allow theirintegration with the bone cut and drill hole data 44 of [block 125].

In one embodiment, the arthritic models 36 may be 3D volumetric modelsas generated from the closed-loop process discussed in U.S. patentapplication Ser. No. 11/959,344 filed by Park. In other embodiments, thearthritic models 36 may be 3D surface models as generated from theopen-loop process discussed in U.S. patent application Ser. No.11/959,344 filed by Park.

In one embodiment, the models 40 of the arthroplasty target areas 42 ofthe arthritic models 36 may be generated via an overestimation processas disclosed in U.S. Provisional Patent Application 61/083,053, which isentitled System and Method for Manufacturing Arthroplasty Jigs HavingImproved Mating Accuracy, was filed by Park Jul. 23, 2008, and is herebyincorporated by reference in its entirety into this DetailedDescription.

As indicated in FIG. 50E, in one embodiment, the data regarding the jigmodels 38 and surface models 40 relative to point W′ (X0-k, Y0-k, Z0-k)is packaged or consolidated as the “jig data” 46 [block 145]. The “jigdata” 46 is then used as discussed below with respect to [block 150] inFIG. 50E.

As can be understood from FIG. 50E, the “saw cut and drill hole data” 44is integrated with the “jig data” 46 to result in the “integrated jigdata” 48 [block 150]. As explained above, since the “saw cut and drillhole data” 44, “jig data” 46 and their various ancestors (e.g., 2Dimages and femur and tibia data of blocks 110-125 and models 36, 38) arematched to each other for position and orientation relative to point Wand W′, the “saw cut and drill hole data” 44 is properly positioned andoriented relative to the “jig data” 46 for proper integration into the“jig data” 46. The resulting “integrated jig data” 48, when provided tothe CNC machine 10, results in jigs 2: (1) configured to matinglyreceive the arthroplasty target areas of the patient's bones; and (2)having cut slots and drill holes that facilitate preparing thearthroplasty target areas in a manner that allows the arthroplasty jointimplants to generally restore the patient's joint line to itspre-degenerated state or natural alignment state.

As can be understood from FIGS. 1A and 50E, the “integrated jig data” 44is transferred from the computer 6 to the CNC machine 10 [block 155].Jig blanks 50 are provided to the CNC machine 10 [block 160], and theCNC machine 10 employs the “integrated jig data” to machine thearthroplasty jigs 2 from the jig blanks 50 [block 165].

For a discussion of example customized arthroplasty cutting jigs 2capable of being manufactured via the above-discussed process, referenceis made to FIGS. 51A-51D. While, as pointed out above, theabove-discussed process may be employed to manufacture jigs 2 configuredfor arthroplasty procedures involving knees, elbows, ankles, wrists,hips, shoulders, vertebra interfaces, etc., the jig examples depicted inFIGS. 51A-51D are for total knee replacement (“TKR”) or partial knee(“uni-knee”) replacement procedures. Thus, FIGS. 51A and 51B are,respectively, bottom and top perspective views of an example customizedarthroplasty femur jig 2A, and FIGS. 51C and 51D are, respectively,bottom and top perspective views of an example customized arthroplastytibia jig 2B.

As indicated in FIGS. 51A and 51B, a femur arthroplasty jig 2A mayinclude an interior side or portion 98 and an exterior side or portion102. When the femur cutting jig 2A is used in a TKR procedure, theinterior side or portion 98 faces and matingly receives the arthroplastytarget area 42 of the femur lower end, and the exterior side or portion102 is on the opposite side of the femur cutting jig 2A from theinterior portion 98.

The interior portion 98 of the femur jig 2A is configured to match thesurface features of the damaged lower end (i.e., the arthroplasty targetarea 42) of the patient's femur 18. Thus, when the target area 42 isreceived in the interior portion 98 of the femur jig 2A during the TKRsurgery, the surfaces of the target area 42 and the interior portion 98match. The cutting jig 2A may include one or more saw guiding slots 123and one or more drill holes 124.

The surface of the interior portion 98 of the femur cutting jig 2A ismachined or otherwise formed into a selected femur jig blank 50A and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged lower end or target area 42 of the patient's femur 18.

As indicated in FIGS. 51C and 51D, a tibia arthroplasty jig 2B mayinclude an interior side or portion 104 and an exterior side or portion106. When the tibia cutting jig 2B is used in a TKR procedure, theinterior side or portion 104 faces and matingly receives thearthroplasty target area 42 of the tibia upper end, and the exteriorside or portion 106 is on the opposite side of the tibia cutting jig 2Bfrom the interior portion 104.

The interior portion 104 of the tibia jig 2B is configured to match thesurface features of the damaged upper end (i.e., the arthroplasty targetarea 42) of the patient's tibia 20. Thus, when the target area 42 isreceived in the interior portion 104 of the tibia jig 2B during the TKRsurgery, the surfaces of the target area 42 and the interior portion 104match.

The surface of the interior portion 104 of the tibia cutting jig 2B ismachined or otherwise formed into a selected tibia jig blank 50B and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged upper end or target area 42 of the patient's tibia 20. Thecutting jig 2B may include one or more saw guiding slots 123 and one ormore drill holes 124.

While the discussion provided herein is given in the context of TKR andTKR jigs and the generation thereof, the disclosure provided herein isreadily applicable to uni-compartmental or partial arthroplastyprocedures in the knee or other joint contexts. Thus, the disclosureprovided herein should be considered as encompassing jigs and thegeneration thereof for both total and uni-compartmental arthroplastyprocedures.

The remainder of this Detailed Discussion will now focus on variousembodiments for performing POP.

B. Overview of Preoperative Planning (“POP”) Procedure

In one embodiment, as can be understood from [blocks 100-110] of FIGS.50A-50C, medical images 16 of the femur and tibia 18, 20 are generated[blocks 100 and 105] and coronal, axial and sagittal image slices areanalyzed to determine reference data 28, 100 z, 900 z. [Block 115]. Thesizes of the implant models 34 are selected relative to the femur andtibia reference data. [Block 112, 114 and 121, 122]. The reference data28, 100 z, 900 z is utilized with the data associated with implantmodels 34 to determine the cut plane location. The joint spacing betweenthe femur and the tibia is determined. An adjustment value tr isdetermined to account for cartilage thickness or joint gap of a restoredjoint. The implant models 34 are shifted or adjusted according to theadjustment value tr [blocks 118 and 123]. Two dimensional computerimplant models 34 are rendered into the two dimensional imaging slice(s)of the bones 28 such that the 2D implant models 34 appear alongside the2D imaging slices of the bones 28. In one embodiment, ITK software,manufactured by Kitware, Inc. of Clifton Park, N.Y. is used to performthis rendering. Once the 2D implant models 34 are rendered into theMRI/CT image, the proper selection, orientation and position of theimplant models can be verified. An additional verification process maybe used wherein 3D models of the bones and implants are created andproper positioning of the implant may be verified. Two dimensionalcomputer models 34 and three dimensional computer models 1004 z, 1006 zof the femur and tibia implants are generated from engineering drawingsof the implants and may be generated via any of the above-referenced 2Dand 3D modeling programs to confirm planning. If the implant sizing isnot correct, then the planning will be amended by further analysis ofthe 2D images. If the implant sizing is accurate, then planning iscomplete. The process then continues as indicated in [block 125] of FIG.50E.

This ends the overview of the POP process. The following discussionswill address each of the aspects of the POP process in detail.

C. Femur and Tibia Images

FIG. 52A depicts 3D bone models or images 28′, 28″ of the femur andtibia 18, 20 from medical imaging scans 16. While FIG. 52A representsthe patient's femur 18 and tibia 20 prior to injury or degeneration(such as, for example, in the case of the femur and tibia restored bonemodels 28A, 28B of FIGS. 42D and 42E), it can be understood that, inother embodiments, the images 28′, 28″ may also represent the patient'sfemur 18 and tibia 20 after injury or degeneration (such as, forexample, the femur bone model 22A in FIG. 44A and the tibia bone model22B in FIG. 44B). More specifically, FIG. 52A is a 3D bone model 28′ ofa femur lower end 200 z and an 3D bone model 28″ of a tibia upper end205 z representative of the corresponding patient bones 18, 20 in anon-deteriorated state and in position relative to each to form a kneejoint 14. The femur lower end 200 z includes condyles 215 z, and thetibia upper end 205 z includes a plateau 220 z. The images or bonemodels 28′, 28″ are positioned relative to each other such that thecurved articular surfaces of the condyles 215 z, which would normallymate with complementary articular surfaces of the plateau 220 z, areinstead not mating, but roughly positioned relative to each other togenerally approximate the knee joint 14.

As generally discussed above with respect to FIGS. 50A-50C, the POPbegins by using a medical imaging process, such as magnetic resonanceimaging (MRI), computed tomography (CT), and/or another other medicalimaging process, to generate imaging data of the patient's knee. Forexample, current commercially available MRI machines use 8 bit (255grayscale) to show the human anatomy. Therefore, certain components ofthe knee, such as the cartilage, cortical bone, cancellous bone,meniscus, etc., can be uniquely viewed and recognized with 255grayscale. The generated imaging data is sent to a preoperative planningcomputer program. Upon receipt of the data, a user or the computerprogram may analyze the data (e.g., two-dimensional MRI images 16, andmore specifically, the 2D femur image(s) 28′ or 2D tibia image(s) 28″)to determine various reference points, reference lines and referenceplanes. In one embodiment, the MRI imaging scans 16 may be analyzed andthe reference data for POP may be generated by a proprietary softwareprogram called PerForm.

For greater detail regarding the methods and systems for computermodeling joint bones, such as the femur and tibia bones forming theknee, please see the following U.S. patent applications, which are allincorporated herein in their entireties: U.S. patent application Ser.No. 11/656,323 to Park et al., titled “Arthroplasty Devices and RelatedMethods” and filed Jan. 19, 2007, now U.S. Pat. No. 9,017,336; U.S.patent application Ser. No. 10/146,862 to Park et al., titled “ImprovedTotal Joint Arthroplasty System” and filed May 15, 2002; U.S. patentSer. No. 11/642,385 to Park et al., titled “Arthroplasty Devices andRelated Methods” and filed Dec. 19, 2006.

FIG. 52B is an isometric view of a computer model of a femur implant 34′and a computer model of a tibia implant 34″ in position relative to eachto form an artificial knee joint 14. The computer models 34′, 34″ may beformed, for example, via computer aided drafting or 3D modelingprograms. As will be discussed later in this detailed description, theimplant computer models may be in 2D or in 3D as necessary for theparticular planning step.

The femur implant model 34′ will have a joint side 240 z and a boneengaging side 245 z. The joint side 240 z will have a condyle-likesurface for engaging a complementary surface of the tibia implant model34″. The bone engaging side 245 z will have surfaces and engagementfeatures 250 z for engaging the prepared (i.e., sawed to shape) lowerend of the femur 18.

The tibia implant model 34″ will have a joint side 255 z and a boneengaging side 260 z. The joint side 255 z will have a plateau-likesurface configured to engage the condyle-like surface of the femurimplant model 34′. The bone engaging side 260 z will have an engagementfeature 265 z for engaging the prepared (i.e., sawed to shape) upper endof the tibia 20.

As discussed in the next subsections of this Detailed Description, thereference data of the femur and tibia bone models or images 28′, 28″ maybe used in conjunction with the implant models 34′, 34″ to select theappropriate sizing for the implants actually to be used for the patient.The resulting selections can then be used for planning purposes, asdescribed later in this Detailed Description.

D. Femur Planning Process

For a discussion of the femur planning process, reference is now made toFIGS. 53-58. FIGS. 53-58 illustrate a process in the POP wherein thesystem 4 utilizes 2D imaging slices (e.g., MRI slices, CT slices, etc.)to determine femur reference data, such as reference points, lines andplanes via their relationship to the trochlear groove plane-GHO of thefemur. The resulting femur reference data 100 z is then mapped orprojected to a y-z coordinate system (sagittal plane). The femurreference data is then applied to a candidate femur implant model,resulting in femoral implant reference data 100 z′. The data 100 z, 100z′ is utilized to select an appropriate set of candidate implants, fromwhich a single candidate implant will be chosen, which selection will bediscussed in more detail below with reference to FIGS. 59-71.

1. Determining Femur Reference Data

For a discussion of a process used to determine the femur referencedata, reference is now made to FIGS. 53-56C. FIG. 53 is a perspectiveview of the distal end of a 3D model 1000 z of the femur image of FIG.52A wherein the femur reference data 100 z is shown. As will beexplained in more detail below, the femur reference data is generated byan analysis of the 2D image scans and FIG. 53 depicts the relativepositioning of the reference data on a 3D model. As shown in FIG. 53,the femur reference data 100 z may include reference points (e.g. D1,D2), reference lines (e.g. GO, EF) and reference planes (e.g. P, S). Thefemur reference data 100 z may be determined by a process illustrated inFIGS. 54A-56D and described in the next sections.

As shown in FIG. 54A, which is a sagittal view of a femur 18illustrating the orders and orientations of imaging slices 16 that areutilized in the femur POP, a multitude of image slices may be compiled.In some embodiments, the image slices may be analyzed to determine, forexample, distal contact points prior to or instead of being compiledinto a bone model. Image slices may extend medial-lateral in planes thatwould be normal to the longitudinal axis of the femur, such as imageslices 1-5 of FIGS. 54A and 55D. Image slices may extend medial-lateralin planes that would be parallel to the longitudinal axis of the femur,such as image slices 6-9 of FIGS. 54A and 56B. The number of imageslices may vary from 1-50 and may be spaced apart in a 2 mm spacing orother spacing.

a. Determining Reference Points P1P2

In some embodiments, the planning process begins with the analysis ofthe femur slices in a 2D axial view. As can be understood from FIG. 54B,which depicts axial imaging slices of FIG. 54A, the series of 2D axialfemur slices are aligned to find the most posterior point of eachcondyle. For example, the most posterior points of slice 5, P1A, P2A,are compared to the most posterior points of slice 4, P1B, P2B. The mostposterior points of slice 4 are more posterior than those of slice 5.Therefore, the points of slice 4 will be compared to slice 3. The mostposterior points of slice 3, P1C, P2C, are more posterior than theposterior points P1B, P2B of slice 4. Therefore, the points of slice 3will be compared to slice 2. The most posterior points of slice 2, P1D,P2D, are more posterior than the posterior points P1C, P2C of slice 3.Therefore, the points of slice 2 will be compared to slice 1. The mostposterior points of slice 1, P1E, P2E, are more posterior than theposterior points P1D, P2D of slice 2. In some embodiments, the points ofslice 1 may be compared to slice 0 (not shown). The most posteriorpoints of slice 0, P1F, P2F, are less posterior than the posteriorpoints P1E, P2E of slice 1. Therefore, the points of slice 1 aredetermined to be the most posterior points P1P2 of the femur. In someembodiments, points P1 and P2 may be found on different axial slices.That is, the most posterior point on the medial side and most posteriorpoint on the lateral side may lie in different axial slices. Forexample, slice 2 may include the most posterior point on the lateralside, while slice 1 may include the most posterior point on the medialside. It can be appreciated that the number of slices that are analyzedas described above may be greater than five slices or less than fiveslices. The points P1, P2 are stored for later analysis.

b. Determining Reference Points D1, D2

The planning process continues with the analysis of the femur slices ina 2D coronal view. As can be understood from FIG. 54C, which depictscoronal imaging slices of FIG. 54A, the series of 2D coronal femurslices are aligned to find the most distal point of each condyle. Forexample, the most distal points of slice 6, D1A, D2A, are compared tothe most distal points of slice 7, D1B, D2B. The most distal points ofslice 7 are more distal than those of slice 6. Therefore, the points ofslice 7 will be compared to slice 8. The most distal points of slice 8,D1C, D2C, are more distal than the distal points D1B, D2B of slice 7.Therefore, the points of slice 8 will be compared to slice 9. The mostdistal points of slice 9, D1D, D2D, are more distal than the distalpoints D1C, D2C of slice 8. In some embodiments, the points of slice 9may be compared to slice 10 (not shown). The most distal points of slice10, D1E, D2E, are less distal than the distal points D1D, D2D of slice9. Therefore, the points of slice 9 are determined to be the most distalpoints D1, D2 of the femur. In some embodiments, points D1 and D2 may befound on different coronal slices. That is, the most distal point on themedial side and most distal point on the lateral side may lie indifferent coronal slices. For example, slice 9 may include the mostdistal point on the lateral side, while slice 8 may include the mostdistal point on the medial side. It can be appreciated that the numberof slices that are analyzed as described above may be greater than fourslices or less than four slices. The points D1, D2 are stored for futureanalysis.

c. Determining Reference Lines CD and GO

Analysis of the 2D slices in the axial view aid in the determination ofinternal/external rotation adjustment. The points D1, D2 represent thelowest contact points of each of the femoral lateral and medial condyles302 z, 303 z. Thus, to establish an axial-distal reference line, lineCD, in 2D image slice(s), the analysis utilizes the most distal point,either D1 or D2, from the undamaged femoral condyle. For example, asshown in FIG. 55A, which is an axial imaging slice of the femur of FIG.54A, when the lateral condyle 302 z is undamaged but the medial condyle303 z is damaged, the most distal point D1 will be chosen as thereference point in establishing the axial-distal reference line, lineCD. The line CD is extended from the lateral edge of the lateralcondyle, through point D1, to the medial edge of the medial condyle. Ifthe medial condyle was undamaged, then the distal point D2 would be usedas the reference point through which line CD would be extended. Thedistal points D1, D2 and line CD are stored for later analysis.

A line CD is verified. A most distal slice of the series of axial viewsis chosen to verify the position of an axial-distal reference line, lineCD. As shown in FIG. 55A, the most distal slice 300 z of the femur(e.g., slice 5 in FIGS. 54A and 55D) is chosen to position line CD suchthat line CD is generally anteriorly-posteriorly centered in the lateraland medial condyles 302 z, 303 z. Line CD is generally aligned with thecortical bone of the undamaged posterior condyle. For example, if themedial condyle 303 z is damaged, the line CD will be aligned with theundamaged lateral condyle, and vice versa. To verify the location ofline CD and as can be understood from FIGS. 53 and 55C, the line CD willalso connect the most distal points D1, D2. The geography information ofline CD will be stored for future analysis.

Line GO is determined. The “trochlear groove axis” or the “trochleargroove reference plane” is found. In the knee flexion/extension motionmovement, the patella 304 z generally moves up and down in the femoraltrochlear groove along the vertical ridge and generates quadricepsforces on the tibia. The patellofemoral joint and the movement of thefemoral condyles play a major role in the primary structure andmechanics across the joint. In a normal knee model or properly alignedknee, the vertical ridge of the posterior patella is generally straight(vertical) in the sliding motion. For the OA patients' knees, there israrely bone damage in the trochlear groove; there is typically onlycartilage damage. Therefore, the trochlear groove of the distal femurcan serve as a reliable bone axis reference. In relation to the jointline assessment, as discussed with reference to FIGS. 63A-63J, thetrochlear groove axis of the distal femur is perpendicular or nearlyperpendicular to the joint line of the knee. A detailed discussion ofthe trochlear groove axis or the trochlear groove reference plane may befound in co-owned U.S. patent application Ser. No. 12/111,924, now U.S.Pat. No. 8,480,679, which is incorporated by reference in its entirety.

To perform the trochlear groove analysis, the MRI slice in the axialview with the most distinct femoral condyles (e.g., the slice with thelargest condyles such as slice 400 z of FIG. 55B) will be chosen toposition the trochlear groove bisector line, line TGB. As shown in FIG.55B, which is an axial imaging slice of the femur of FIG. 54A, the mostdistinct femoral condyles 302 z, 303 z are identified. The trochleargroove 405 z is identified from image slice 400 z. The lowest extremity406 z of the trochlear groove 405 z is then identified. Line TGB is thengenerally aligned with the trochlear groove 405 z across the lowestextremity 406 z. In addition, and as shown in FIG. 55D, which is theaxial imaging slices 1-5 taken along section lines 1-5 of the femur inFIG. 54A, each of the slices 1-5 can be aligned vertically along thetrochlear groove 405 z, wherein points G1, G2, G3, G4, G5 respectivelyrepresent the lowest extremity 406 z of trochlear groove 405 z for eachslice 1-5. By connecting the various points G1, G2, G3, G4, G5, a pointO can be obtained. As can be understood from FIGS. 53 and 55C, resultingline GO is perpendicular or nearly perpendicular to line D1 D2. In a 90°knee extension, line GO is perpendicular or nearly perpendicular to thejoint line of the knee and line P1P2. Line GO is stored for lateranalysis.

d. Determining Reference Lines EF and HO

Analysis of the 2D slices in the coronal view aid in the determinationof femoral varus/valgus adjustment. The points P1, P2 determined aboverepresent the most posterior contact points of each of the femorallateral and medial condyles 302 z, 303 z. Thus, to establish a coronalposterior reference line, line EF, in 2D image slice(s), the analysisutilizes the most posterior point, either P1 or P2, from the undamagedfemoral condyle. For example, as shown in FIG. 56A, when the lateralcondyle 302 z is undamaged but the medial condyle 303 z is damaged, themost posterior point P1 will be chosen as the reference point inestablishing the coronal posterior reference line, line EF. The line EFis extended from the lateral edge of the lateral condyle, through pointP1, to the medial edge of the medial condyle. If the medial condyle wasundamaged, then the posterior point P2 would be used as the referencepoint through which line EF would be extended. The posterior points P1,P2 and line EF are stored for later analysis.

The points, P1P2 were determined as described above with reference toFIG. 54B. Line EF is then verified. A most posterior slice of the seriesof coronal views is chosen to verify the position of a coronal posteriorreference line, line EF. As shown in FIG. 56A, which is a coronalimaging slice of FIG. 54A, the most posterior slice 401 of the femur(e.g., slice 6 in FIGS. 54A and 56B) is chosen to position line EF suchthat line EF is generally positioned in the center of the lateral andmedial condyles 302 z, 303 z. Line EF is generally aligned with thecortical bone of the undamaged posterior condyle. For example, if themedial condyle 303 z is damaged, the line EF will be aligned with theundamaged lateral condyle, and vice versa. To verify the location ofline EF and as can be understood from FIG. 53, the line EF will alsoconnect the most posterior points P1, P2. The geography information ofline EF will be stored for future analysis.

In some embodiments, line HO may be determined. As shown in FIG. 56B,which are coronal imaging slices 6-9 taken along section lines 6-9 ofthe femur in FIG. 54A, each of the image slices 6-9 taken from FIG. 54Acan be aligned along the trochlear groove. The points H6, H7, H8, H9respectively represent the lowest extremity of the trochlear groove foreach of the image slices 6-8 from FIG. 54A. By connecting the variouspoints H6, H7, H8, the point O can again be obtained. The resulting lineHO is established as the shaft reference line-line SHR. Thecoronal-posterior reference line, line EF and coronal-distal referenceline, line AB may be adjusted to be perpendicular or nearlyperpendicular to the shaft reference line-line SHR (line HO). Thus, theshaft reference line, line SHR (line HO) is perpendicular or nearlyperpendicular to the coronal-posterior reference line, line EF and tothe coronal-distal reference line, line AB throughout the coronal imageslices.

As can be understood from FIGS. 53 and 56B, the trochlear grooveplane-GHO, as the reference across the most distal extremity of thetrochlear groove of the femur and in a 90° knee extension, should beperpendicular to line AB. The line-HO, as the reference across the mostposterior extremity of trochlear groove of the femur and in a 0° kneeextension, should be perpendicular to line AB.

e. Determining Reference Line AB and Reference Planes P and S

As can be understood from FIG. 53, a posterior plane S may beconstructed such that the plane S is normal to line GO and includesposterior reference points P1, P2. A distal plane P may be constructedsuch that it is perpendicular to posterior plane S and may includedistal reference points D1, D2 (line CD). Plane P is perpendicular toplane S and forms line AB therewith. Line HO and line GO areperpendicular or nearly perpendicular to each other. Lines CD, AB and EFare parallel or nearly parallel to each other. Lines CD, AB and EF areperpendicular or nearly perpendicular to lines HO and GO and thetrochlear plane GHO.

f. Verification of the Femoral Reference Data

As shown in FIG. 56C, which is an imaging slice of the femur of FIG. 54Ain the sagittal view, after the establishment of the reference linesfrom the axial and coronal views, the axial-distal reference line CD andcoronal-posterior reference line EF and planes P, S are verified in the2D sagittal view. The sagittal views provide the extension/flexionadjustment. Thus, as shown in FIG. 56C, slice 800 z shows a sagittalview of the femoral medial condyle 303 z. Line-bf and line-bd intersectat point-b. As can be understood from FIGS. 53 and 56C, line-bf falls onthe coronal plane-S, and line-bd falls on the axial plane-P. Thus, inone embodiment of POP planning, axial and coronal views are used togenerate axial-distal and coronal-posterior reference lines CD, EF.These two reference lines CD, EF can be adjusted (via manipulation ofthe reference data once it has been imported and opened on the computer)to touch in the black cortical rim of the femur. The adjustment of thetwo reference lines on the femur can also be viewed simultaneously inthe sagittal view of the MRI slice, as displayed in FIG. 56C. Thus, thesagittal view in FIG. 56C provides one approach to verify if the tworeference lines do touch or approximately touch with the femur corticalbone. In some embodiments, line-bf is perpendicular or nearlyperpendicular to line-bd. In other embodiments, line bf may not beperpendicular to bd. This angle depends at least partially on therotation of femoral bone within MRI.

With reference to FIGS. 53-56C, in one embodiment, lines HO and GO maybe within approximately six degrees of being perpendicular with linesP1P2, D1D2 and A1A2 or the preoperative planning for the distal femurwill be rejected and the above-described processes to establish thefemoral reference data 100 z (e.g. reference lines CD, EF, AB, referencepoints P1P2, D1D2) will be repeated until the femoral reference datameets the stated tolerances, or a manual segmentation for setting up thereference lines will be performed. In other embodiments, if there aremultiple failed attempts to provide the reference lines, then thereference data may be obtained from another similar joint that issufficiently free of deterioration. For example, in the context ofknees, if repeated attempts have been made without success to determinedreference data in a right knee medial femur condyle based on dataobtained from the right knee lateral side, then reference data could beobtained from the left knee lateral or medial sides for use in thedetermination of the femoral reference data.

g. Mapping the Femoral Reference Data to a Y-Z Plane

As can be understood from FIGS. 56D-58, the femoral reference data 100 zwill be mapped to a y-z coordinate system to aid in the selection of anappropriate implant. As shown in FIGS. 56D-56E, which are axial andcoronal slices, respectively, of the femur, the points D1D2 of thedistal reference line D1D2 or CD were determined from both a 2D axialview and a 2D coronal view and therefore are completely defined in 3D.Similarly, the points P1P2 of the posterior reference line P1P2 or EFwere determined from both a 2D axial view and a 2D coronal view andtherefore are completely defined in 3D.

As shown in FIG. 57, which is a posterior view of a femur 3D model 1000z, the reference data 100 z determined by an analysis of 2D images maybe imported onto a 3D model of the femur for verification purposes. Thedistance L between line EF and line CD can be determined and stored forlater analysis during the selection of an appropriate implant size.

As indicated in FIG. 58, which depicts a y-z coordinate system, theposterior points P1P2 and distal points D1D2 of the 2D images 28′ mayalso be projected onto a y-z plane and this information is stored forlater analysis.

2. Determining Femoral Implant Reference Data

There are 6 degrees of freedom for a femoral implant to be moved androtated for placement on the femoral bone. The femur reference data 100z (e.g. points P1P2, D1D2, reference lines EF, CD, reference planes P,S) is utilized in the selection and placement of the femoral implant.For a discussion of a process used to determine the implant referencedata, reference is now made to FIGS. 59-71.

a. Map Femur Reference Data to Implant Model to Establish FemoralImplant Reference Data

As shown in FIGS. 59 and 60, which are perspective views of a femoralimplant model 34′, the femur reference data 100 z may be mapped to a 3Dmodel of the femur implant model 34′ in a process of POP. The femurreference data 100 z and the femur implant model 34′ are openedtogether. The femur implant model 34′ is placed on a 3D coordinatesystem and the data 100 z is also transferred to that coordinate systemthereby mapping the data 100 z to the model 34′ to create femoralimplant data 100 z′. The femoral implant data 100 z′ includes anaxial-distal reference line (line-C′D′) and a coronal-posteriorreference line (line-E′F′).

As can be understood from FIGS. 59 and 60, distal line-D1′D2′ representsthe distance between the two most distal points D1′, D2′. Posteriorline-P1′P2′ represents the distance between the two most posteriorpoints P1′, P2′. The lines-D1′D2′ P1′P2′ of the implant model 34′ can bedetermined and stored for further analysis.

As shown in FIG. 61, which shows a coordinate system wherein some of thefemoral implant reference data 100 z′ is shown, the endpoints D1′D2′ andP1′P2′ may also be projected onto a y-z plane and this information isstored for later analysis. As shown in FIG. 62, the implant referencedata 100 z′ may also be projected onto the coordinate system. Thedistance L′ between line E′F′ and line C′D′, and more specificallybetween lines D1′D2′, P1′P2′ can be determined and stored for later useduring the selection of an implant.

3. Determining Joint Line and Adjustment to Implant that Allows CondylarSurfaces of Implant Model to Restore Joint to Natural Configuration

In order to allow an actual physical arthroplasty implant to restore thepatient's knee to the knee's pre-degenerated or natural configurationwith the natural alignment and natural tensioning in the ligaments, thecondylar surfaces of the actual physical implant generally replicate thecondylar surfaces of the pre-degenerated joint bone. In one embodimentof the systems and methods disclosed herein, condylar surfaces of the 2Dimplant model 34′ are matched to the condylar surfaces of the 2D bonemodel or image 28′. However, because the bone model 28′ may be bone onlyand not reflect the presence of the cartilage that actually extends overthe pre-degenerated condylar surfaces, the alignment of the implant 34′may be adjusted to account for cartilage or proper spacing between thecondylar surfaces of the cooperating actual physical implants (e.g., anactual physical femoral implant and an actual physical tibia implant)used to restore the joint such that the actual physical condylarsurfaces of the actual physical cooperating implants will generallycontact and interact in a manner substantially similar to the way thecartilage covered condylar surfaces of the pre-degenerated femur andtibia contacted and interacted. Thus, in one embodiment, the implantmodels are modified or positionally adjusted to achieve the properspacing between the femur and tibia implants.

a. Determine Adjustment Value tr

To achieve the correct adjustment, an adjustment value tr may bedetermined. In one embodiment, the adjustment value tr may be determinedin 2D by a calipers measuring tool (a tool available as part of thesoftware). The calipers tool is used to measure joint spacing betweenthe femur and the tibia by selection of two points in any of the 2D MRIviews and measuring the actual distance between the points. In anotherembodiment, the adjustment value tr that is used to adjust the implantduring planning may be based off of an analysis associated withcartilage thickness. In another embodiment, the adjustment value tr usedto adjust the implant during planning may be based off of an analysis ofproper joint gap spacing. Both the cartilage thickness and joint gapspacing methods are discussed below in turn.

i. Determining Cartilage Thickness and Joint Line

FIG. 63A shows the femoral condyle 310 z and the proximal tibia of theknee in a sagittal MRI image slice. The distal femur 28′ is surroundedby the thin black rim of cortical bone. Due to the nature of irregularbone and cartilage loss in OA patients, it can be difficult to find theproper joint line reference for the models used during the POP.

The space between the elliptical outlining 325 z′, 325 z″ along thecortical bone represents the cartilage thickness of the femoral condyle310 z. The ellipse contour of the femoral condyle 310 z can be seen onthe MRI slice shown in FIG. 63A and obtained by a three-point tangentcontact spot (i.e., point t1, t2, t3) method. In a normal, healthy knee,the bone joint surface is surrounded by a layer of cartilage. Becausethe cartilage is generally worn-out in OA and the level of cartilageloss varies from patient to patient, it may be difficult to accuratelyaccount for the cartilage loss in OA patients when trying to restore thejoint via TKA surgery. Therefore, in one embodiment of the methodologyand system disclosed herein, a minimum thickness of cartilage isobtained based on medical imaging scans (e.g., MRI, etc.) of theundamaged condyle. Based on the cartilage information, the joint linereference can be restored. For example, the joint line may be line 630 zin FIG. 63B.

The system and method disclosed herein provides a POP method tosubstantially restore the joint line back to a “normal or natural knee”status (i.e., the joint line of the knee before OA occurred) andpreserves ligaments in TKA surgery (e.g., for a total knee arthroplastyimplant) or partial knee arthroplasty surgery (e.g., for a uni-kneeimplant).

FIG. 63B is a coronal view of a knee model in extension. As depicted inFIG. 63B, there are essentially four separate ligaments that stabilizethe knee joint, which are the medial collateral ligament (MCL), anteriorcruciate ligament (ACL), lateral collateral ligament (LCL), andposterior cruciate ligament (PCL). The MCL and LCL lie on the sides ofthe joint line and serve as stabilizers for the side-to-side stabilityof the knee joint. The MCL is a broader ligament, whereas the LCL is adistinct cord-like structure.

The ACL is located in the front part of the center of the joint. The ACLis a very important stabilizer of the femur on the tibia and serves toprevent the tibia from rotating and sliding forward during agility,jumping, and deceleration activities. The PCL is located directly behindthe ACL and serves to prevent the tibia from sliding to the rear. Thesystem and method disclosed herein provides POP that allows thepreservation of the existing ligaments without ligament release duringTKA surgery. Also, the POP method provides ligament balance, simplifyingTKA surgery procedures and reducing pain and trauma for OA patients.

As indicated in FIG. 63B, the joint line reference 630 z is definedbetween the two femoral condyles 302 z, 303 z and their correspondingtibia plateau regions 404 z, 406 z. Area A illustrates a portion of thelateral femoral condyle 302 z and a portion of the corresponding lateralplateau 404 z of tibia 205 z. Area B illustrates the area of interestshowing a portion of the medial femoral condyle 303 z and a portion ofthe corresponding medial plateau 406 z of tibia 205 z.

FIGS. 63C, 63D and 63F illustrate MRI segmentation slices for joint lineassessment. FIG. 63E is a flow chart illustrating the method fordetermining cartilage thickness used to determine proper joint line. Thedistal femur 200 z is surrounded by the thin black rim of cortical bone645 z. The cancellous bone (also called trabecular bone) 650 z is aninner spongy structure. An area of cartilage loss 655 z can be seen atthe posterior distal femur. For OA patients, the degenerative cartilageprocess typically leads to an asymmetric wear pattern that results inone femoral condyle with significantly less articulating cartilage thanthe other femoral condyle. This occurs when one femoral condyle isoverloaded as compared to the other femoral condyle.

As can be understood from FIGS. 63C, 63E and 63F, the minimum cartilagethickness is observed and measured for the undamaged and damaged femoralcondyle 302 z, 303 z [block 1170]. If the greatest cartilage loss isidentified on the surface of medial condyle 303 z, for example, then thelateral condyle 302 z can be used as the cartilage thickness referencefor purposes of POP. Similarly, if the greatest cartilage loss isidentified on the lateral condyle 302 z, then the medial condyle 303 zcan be used as the cartilage thickness reference for purposes of POP. Inother words, use the cartilage thickness measured for the least damagedcondyle cartilage as the cartilage thickness reference for POP [block1175].

As indicated in FIG. 63D, the thickness of cartilage can be analyzed inorder to restore the damaged knee compartment back to its pre-OA status.In each of the MRI slices taken in regions A and B in FIG. 63B, thereference lines as well as the major and minor axes 485 z, 490 z ofellipse contours 480 z′, 480 z″ in one femoral condyle 303 z can beobtained.

As shown in FIG. 63F, for the three-point method, the tangents are drawnon the condylar curve at zero degrees and 90 degrees articular contactpoints. The corresponding tangent contact spots t1 and t2 are obtainedfrom the tangents. The line 1450 z perpendicular to the line 1455 zdetermines the center of the ellipse curve, giving the origin of (0, 0).A third tangent contact spot t3 can be obtained at any point along theellipse contour between the zero degree, t1 point and the 90 degree, t2point. This third spot t3 can be defined as k, where k=1 to n points.

The three-point tangent contact spot analysis may be employed toconfigure the size and radius of the condyle 303 z of the femur bonemodel 28′. This provides the “x” coordinate and “y” coordinate, as the(x, y) origin (0, 0) shown in FIG. 63D. The inner ellipse model 480 z′of the femoral condyle shows the femoral condyle surrounded by corticalbone without the cartilage attached. The minimum cartilage thicknesstmmin outside the inner ellipse contour 480 z′ is measured. Based on theanalysis of the inner ellipse contour 480 z′ (i.e., the bone surface)and outer ellipse contour 480 z″ (i.e., the cartilage surface) of theone non-damaged condyle of the femur bone model 28′, the inner ellipsecontour 480 z′ (i.e., the bone surface) and the outer ellipse contour480 z″ (i.e., the cartilage surface) of the other condyle (i.e., thedamage or deteriorated condyle) may be determined.

As can be understood from FIGS. 63B and 63D, ellipse contours 480 z′,480 z″ are determined in areas A and B for the condyles 302 z, 303 z ofthe femur bone model 28′. The inner ellipse contour 480 z′, representingthe bone-only surface, and the outer ellipse contour 480 z″,representing the bone-and-cartilage surface, can be obtained. Theminimum cartilage thickness tmmin is measured based on the cartilagethickness tr between the inner ellipse 480 z′ and outer ellipse 480 z″.MRI slices of the two condyles 302 z, 303 z of the femur bone model 28′in areas A and B are taken to compare the respective ellipse contours inareas A and B. If the cartilage loss is greatest at the medial condyle303 z in the MRI slices, the minimum thickness tmmin for the cartilagecan be obtained from the lateral condyle 302 z. Similarly, if thelateral condyle 302 z has the greatest cartilage loss, the cartilagethickness tmmin can be obtained from undamaged medial condyle 303 z ofthe femur restored bone model 28′. The minimum cartilage can beillustrated in the formula, tmmin=MIN (ti), where i=1 to k.

ii. Determining Joint Gap

As mentioned above, in one embodiment, the adjustment value tr may bedetermined via a joint line gap assessment. The gap assessment may serveas a primary estimation of the gap between the distal femur and proximaltibia of the bone images. The gap assessment may help achieve properligament balancing.

In one embodiment, an appropriate ligament length and joint gap may notbe known from the 2D bone models or images 28′, 28″ (see, e.g. FIG. 52B)as the bone models or images may be oriented relative to each other in afashion that reflects their deteriorated state. For example, as depictedin FIG. 63J, which is a coronal view of bone models 28′, 28″ oriented(e.g., tilted) relative to each other in a deteriorated stateorientation, the lateral side 1487 z was the side of the deteriorationand, as a result, has a greater joint gap between the distal femur andthe proximal tibia than the medial side 1485 z, which was thenon-deteriorated side of the joint in this example.

In one embodiment, ligament balancing may also be considered as a factorfor selecting the appropriate implant size. As can be understood fromFIG. 63J, because of the big joint gap in the lateral side 1487 z, thepresumed lateral ligament length (L1+L2+L3) may not be reliable todetermine proper ligament balancing. However, the undamaged side, whichin FIG. 63J is the medial side 1485 z, may be used in some embodimentsas the data reference for a ligament balancing approach. For example,the medial ligament length (M1+M2+M3) of the undamaged medial side 1485z may be the reference ligament length used for the ligament balancingapproach for implant size selection.

In one embodiment of the implant size selection process, it may beassumed that the non-deteriorated side (i.e., the medial side 1485 z inFIG. 63J in this example) may have the correct ligament length forproper ligament balancing, which may be the ligament length of(M1+M2+M3). When the associated ligament length (“ALL”) associated witha selected implant size equals the correct ligament length of(M1+M2+M3), then the correct ligament balance is achieved, and theappropriate implant size has been selected. However, when the ALL endsup being greater than the correct ligament length (M1+M2+M3), theimplant size associated with the ALL may be incorrect and the nextlarger implant size may need to be selected for the design of thearthroplasty jig 2.

For a discussion regarding the gap assessment, which may also be basedon ligament balance off of a non-deteriorated side of the joint,reference is made to FIGS. 63G and 63H. FIGS. 63G and 63H illustratecoronal views of the bone models 28′, 28″ in their post-degenerationalignment relative to each as a result of OA or injury. As shown in FIG.63G, the tibia model 28″ is tilted away from the lateral side 1487 z ofthe knee 1486 z such that the joint gap between the femoral condylarsurfaces 1490 z and the tibia condylar surfaces 1491 z on the lateralside 1487 z is greater than the joint gap on the medial side 1485 z.

As indicated in FIG. 63H, which illustrates the tibia in a coronal crosssection, the line 1495 z may be employed to restore the joint line ofthe knee 1486 z. The line 1495 z may be caused to extend across each oflowest extremity points 1496 z, 1497 z of the respective femoral lateraland medial condyles 1498 z, 1499 z. In this femur bone model 28′, line1495 z may be presumed to be parallel or nearly parallel to the jointline of the knee 1486 z.

As illustrated in FIG. 63H, the medial gap Gp2 represents the distancebetween the distal femoral medial condyle 1499 z and the proximal tibiamedial plateau 1477 z. The lateral gap Gp1 represents the distancebetween the distal femoral lateral condyle 1498 z and the proximal tibialateral plateau 1478 z. In this example illustrated in FIG. 63H, thelateral gap Gp1 is significantly larger than the medial gap Gp2 due todegeneration caused by injury, OA, or etc., that occurred in the lateralside 1487 z of the knee 1486 z. It should be noted that the alignment ofthe bone models 28′, 28″ relative to each other for the exampleillustrated in FIGS. 63G and 63H depict the alignment the actual boneshave relative to each other in a deteriorated state. To restore thejoint line reference and maintain ligament balancing for the medialcollateral ligament (MCL) and lateral collateral ligament (LCL), thejoint line gap Gp3 that is depicted in FIG. 63I, which is the same viewas FIG. 63G, except with the joint line gap Gp3 in a restored state, maybe used for the joint spacing compensation adjustment as describedbelow. As illustrated in FIG. 63I, the lines 1495 z and 1476 zrespectively extend across the most distal contact points 1496 z, 1497 zof the femur condyles 1498 z, 1499 z and the most proximal contactpoints 1466 z, 1467 z of the tibia plateau condyles 1477 z, 1478 z.

For calculation purposes, the restored joint line gap Gp3 may bewhichever of Gp1 and Gp2 has the minimum value. In other words, therestored joint line gap Gp3 may be as follows: Gp3=MIN (Gp1, Gp2). Forpurposes of the adjustment for joint spacing compensation, theadjustment value tr may be calculated as being half of the value forGp3, or in other words, tr=Gp3/2. As can be understood from FIGS.63G-63H and 14J, in this example, the non-deteriorated side 1485 z hasGp2, which is the smallest joint line gap and, therefore, Gp3=Gp2 in theexample depicted in FIG. 63G-14J, and tr=Gp2/2.

In one embodiment, the joint line gap assessment may be at least a partof a primary assessment of the geometry relationship between the distalfemur and proximal tibia. In such an embodiment, the joint gapassessment step may occur prior to the femur planning steps of the POPprocess. However, in other embodiments, the joint line gap assessmentmay occur at other points along the overall POP process.

b. Determine Compensation for Joint Spacing

Once the adjustment value tr is determined based off of cartilagethickness or joint line gap Gp3, the planning for the femoral implantmodel 34′ can be modified or adjusted to compensate for the jointspacing in order to restore the joint line. As shown in FIG. 64, whichis a 3D coordinate system wherein the femur reference data 100 z isshown, the compensation for the joint spacing is performed both indistal and posterior approaches. Thus, the joint compensation pointsrelative to the femur reference data are determined. As will bediscussed later in this Detailed Description, the joint compensationpoints relative to the femur reference data will be used to determinethe joint compensation relative to the femur implant.

As can be understood from FIG. 65, which is a y-z plane wherein thejoint compensation points are shown, the posterior plane S and thedistal plane P are moved away in the direction of normal of plane S andP respectively by the adjustment value tr. In one embodiment, theadjustment value tr is equal to the cartilage thickness. That is, thejoint compensation points will be determined relative to the posteriorplane S and the distal plane P which are moved away in the direction ofnormal of plane S and P, respectively, by an amount equal to thecartilage thickness. In some embodiments, the adjustment value tr isequal to one-half of the joint spacing. That is, the joint compensationpoints will be determined relative to the posterior plane S and thedistal plane P which are moved away in the direction of normal of planeS and P, respectively, by an amount equal one-half the joint spacing. Inother words, the femoral implant accounts for half of the joint spacingcompensation, while the tibia implant will account for the other half ofthe joint spacing compensation.

As can be understood from FIG. 64, the femur reference data 100 z wasuploaded onto a coordinate system, as described above. To compensate forthe joint spacing, the distal line-D1D2 is moved closer to the distalplane-P by an amount equal to the adjustment value tr, thereby resultingin joint spacing compensation points D1J, D2J and line D1JD2J. Thedistal plane P was previously moved by adjustment value tr. Similarly,posterior reference line P1P2 is moved closer to the posterior plane-Sby an amount equal to the adjustment value tr, thereby resulting injoint spacing compensation points P1J, P2J and line P1JP2J. Thetrochlear groove reference line-line GO does not move and remains as thereference line for the joint spacing compensation. Lines D1JD2J andP1JP2J will be stored and utilized later for an analysis related to thefemoral implant silhouette curve.

4. Selecting the Sizes for the Femoral Implants

The next steps are designed to select an appropriate implant size suchthat the implant will be positioned within the available degrees offreedom and may be optimized by 2D optimization. There are 6 degrees offreedom for a femoral implant to be moved and rotated for placement onthe femur. For example, the translation in the x direction is fixedbased on the reference planes-S and P and sagittal slices of femur asshown in FIGS. 53 and 63C. Rotation around the y axis, which correspondsto the varus/valgus adjustment is fixed based on the reference linesdetermined by analysis of the coronal slices, namely, lines EF and AB,and coronal plane-S as shown in FIGS. 53 and 56B. Rotation around the zaxis, which corresponds to internal/external rotation, is fixed by thetrochlear groove reference line, line GO or TGB, axial-distal referenceline, line CD, and axial-posterior reference line, line AB, as shown inthe axial views in FIGS. 53 and 55A-55E. By fixing these three degreesof freedom, the position of the implant can be determined so that theouter silhouette line of the implant passes through both the distalreference line and posterior reference line. Optimization will searchfor a sub-optimal placement of the implant such that an additional angleof flange contact is greater than but relatively close to 7 degrees.Thus, by constraining the 3 degrees of freedom, the appropriate implantcan be determined.

a. Overview of Selection of Femoral Implant

Based on previously determined femoral implant data 100 z′, as shown inFIGS. 60-62, a set of 3 possible sizes of implants are chosen. For eachimplant, the outer 2D silhouette curve of the articular surface of thecandidate implant model is computed and projected onto a y-z plane, asshown in FIGS. 69A-69C. The calculated points of the silhouette curveare stored. Then, the sagittal slice corresponding to the inflectionpoint 500 z (see FIG. 70A) is found and the corresponding segmentationspline is considered and the information is stored. Then an iterativeclosest point alignment is devised to find the transform to match theimplant to the femur.

The next sections of this Detailed Description will now discuss theprocess for determining the appropriate implant candidate, withreference to FIGS. 66-71.

i. Implant Selection

In one embodiment, there is a limited number of sizes of a candidatefemoral implant. For example, one manufacturer may supply six sizes offemoral implants and another manufacturer may supply eight or anothernumber of femoral implants. A first implant candidate 700 z (see FIG.66) may be chosen based on the distance L′ between the posterior anddistal reference lines P1′P2′ and DIM′ determined above in FIG. 62, withreference to the femoral implant reference data 100 z′. The distance L′of the candidate implants may be stored in a database and can beretrieved from the implant catalogue. In some embodiments, a second andthird implant candidate 702, 704 (not shown) may be chosen based on thedistance L between the posterior and distal reference lines P1P2 andD1D2 of the femur 28′ determined above in FIG. 57, with reference to thefemoral reference data 100 z and distance L′. First implant candidate700 z has the same distance L as the patient femur. Second implantcandidate 702 is one size smaller than the first implant candidate 700z. Third implant candidate 704 is one size larger than the first implantcandidate 700 z. In some embodiments, more than 3 implant candidates maybe chosen.

The following steps 2-6 are performed for each of the implant candidates700 z, 702, 704 in order to select the appropriate femoral implant 34′.

ii. Gross Alignment of Implant onto Femur

In some embodiments, the gross alignment of the implant 34′ onto thefemur 28′ may be by comparison of the implant reference data 100 z′ andthe femur reference data 100 z. In some embodiments, gross alignment maybe via comparison of the medial-lateral extents of both the implant andthe femur. In some embodiments, both gross alignment techniques may beused.

In some embodiments, as shown in FIG. 66, which shows the implant 34′placed onto the same coordinate plane with the femur reference data 100z, the implant candidate may be aligned with the femur. Alignment withthe femur may be based on the previously determined implant referencelines D1′D2′ and P1′P2′ and femur reference lines D1D2 and P1P2.

In some embodiments, and as can be understood from FIGS. 67A-67C and68A-68C, the medial lateral extent of the femur and the implant can bedetermined and compared to ensure the proper initial alignment. FIG. 67Ais a plan view of the joint side 240 z of the femur implant model 34′depicted in FIG. 52B. FIG. 67B is an axial end view of the femur lowerend 200 z of the femur bone model 28′ depicted in FIG. 52A. The viewsdepicted in FIGS. 67A and 67B are used to select the proper size for thefemoral implant model 34′.

As can be understood from FIG. 67A, each femoral implant available viathe various implant manufacturers may be represented by a specificfemoral implant 3D computer model 34′ having a size and dimensionsspecific to the actual femoral implant. Thus, the representative implantmodel 34′ of FIG. 67A may have an associated size and associateddimensions in the form of, for example, an anterior-posterior extent iAPand medial-lateral extent iML, which data can be computed and stored ina database. These implant extents iAP, iML may be compared to thedimensions of the femur slices from the patient's actual femur 18. Forexample, the femur bone 18 may have dimensions such as, for example, ananterior-proximal extent bAP and a medial-lateral extent bML, as shownin FIG. 67B. In FIG. 67A, the anterior-posterior extent iAP of thefemoral implant model 34′ is measured from the anterior edge 270 z tothe posterior edge 275 z of the femoral implant model 34′, and themedial-lateral extent iML is measured from the medial edge 280 z to thelateral edge 285 z of the femoral implant model 34′.

Each patient has femurs that are unique in size and configuration fromthe femurs of other patients. Accordingly, each femur slice will beunique in size and configuration to match the size and configuration ofthe femur medically imaged. As can be understood from FIG. 67B, thefemoral anterior-posterior length bAP is measured from the anterior edge290 z of the patellofemoral groove to the posterior edge 295 of thefemoral condyle, and the femoral medial-lateral length bML is measuredfrom the medial edge 301 z of the medial condyle to the lateral edge 305z of the lateral condyle. The implant extents iAP and iML and the femurextents bAP, bML may be aligned for proper implant placement as shown inFIG. 67C and along the direction of axial-distal reference line-CD.

As can be understood from FIGS. 68A-68C, these medial-lateral extents ofthe implant iML and femur bML can be measured from the 2D slices of thefemur of FIG. 54A. For example, FIG. 68A, which shows the most medialedge of the femur in a 2D sagittal slice and FIG. 68B, which shows themost lateral edge of the femur in a 2D sagittal slice, can be used tocalculate the bML of the femur 28′. The implant 34′ will be centeredbetween the medial and lateral edges, as shown in FIG. 68C, which is a2D slice in coronal view showing the medial and lateral edges, therebygrossly aligning the implant with the femur.

iii. Determine Outer Silhouette Curve of Implant in Y-Z Plane

The silhouette of the femoral implant is the curve formed by farthestpoints from center in y-z plane projection of the femoral implantgeometry. The points of the silhouette curve may be utilized to confirmplacement of the implant onto the femur based on the femur referencelines that have been altered to account for the joint compensation.

For a discussion of the process for determining the points of thesilhouette curve of the femoral implant, reference is now made to FIGS.69A-69C. As can be understood from FIG. 69A, which is an implant 34′mapped onto a y-z plane, the points of a candidate implant are retrievedfrom the implant database. The points are then imported onto a y-z planeand the silhouette curve can be determined. The silhouette curve 34′″ isdetermined by finding the points that are the farthest from the centeralong an outer circumference 35 of the articular surface of the implant34′. FIG. 69B, which is the silhouette curve 34′″ of the implant 34′,shows the result of the silhouette curve calculations. The silhouettecurve data is then imported into a y-z plane that includes the jointspacing compensation data, as shown in FIG. 69C, which is the silhouettecurve 34′″ aligned with the joint spacing compensation points D1JD2J andP1JP2J. The resulting joint spacing compensation and silhouette curvedata 800 z (e.g. D1′″D2” ″P1′″P2′″) is stored for later analysis.

iv. Determination of Inflection Point, Flange Point, Femur Spline andAnterior Femur Cut Plane

The flange point is determined and stored. As can be understood fromFIG. 70A, which shows a distal femur 28′ with an implant 34′, the distalfemur is analyzed and the flange point 500 z of the implant 34′ isdetermined relative to the anterior surface 502 z of the distal end of afemur condyle 28′. FIG. 70B, which depicts a femur implant 34′,illustrates the location of the flange point 500 z on the implant 34′ asdetermined by an analysis such as one illustrated in FIG. 70A.

The anterior cut plane 504 z is determined and stored. The range of theanterior cut plane of the implant is determined such that the cut plane(and therefore the implant) is within certain tolerances. As shown inFIG. 70A, a cut plane 504 z is determined based on the location of theimplant 34′ on the femur 28′. An angle A between the cut plane 504 z andthe flange point 500 z is between approximately 7 and approximately 15degrees. In some embodiments, the angle A is approximately 7 degrees. Insome embodiments, the distal cut plane may be found as described belowwith respect to the final verification step. For each respectiveimplant, the anterior cut plane and the distal cut plane are at a fixedangle for the implant. That is, once the anterior cut plane is found,the distal cut plane can be determined relative to the fixed angle andthe anterior cut plane. Alternatively, once the distal cut plane isfound, the anterior cut plane can be determined relative to the fixedangle and the distal cut plane.

The inflection point 506 z is determined and stored. As shown in FIG.70C, which shows a slice of the distal femur 28′ in the sagittal view,the inflection point 506 z is located on the anterior shaft of thespline 508 of femur 28′ where the flange point 500 z of the implant 34′is in contact with the femur 28′. An implant matching algorithm willmatch the flange point 500 z of implant 34′ to the spline 508 of thefemur at approximately the inflection point 506 z of the femur 28′. Ascan be understood from FIG. 70D, which shows the implant 34′ positionedon the femur 28′, the implant 34′ should be aligned to touch the distaland posterior reference planes P, S respectively to reach properalignment. In one embodiment, the implant matching algorithm is acustomized extension of an algorithm known as iterative closest pointmatching.

The next section of the Detailed Description now discusses how the dataand data points determined above and stored for future analysis will beused in the selection of an appropriate implant.

v. Determine Points of Set A and Set B

Determination of the data sets contained in Set A and Set B aid indetermining the appropriate implant and ensuring that the chosen implantmates with the femur within certain tolerances.

The joint spacing compensation points D1JD2J and P1JP2J were determinedas described with reference to FIG. 65 and are added to Set A. Next, thejoint spacing compensation points D1JD2J and P1JP2J are matched to theclosest respective points on the silhouette curve, as shown in FIG. 69C,thereby resulting in points D1′″D2′″ and P1′″P2′″ or the joint spacingcompensation and silhouette curve data 800 z. Points D1′″D2′″ andP1′″P2′″ will be added to Set B.

The inflection point and flange point data are analyzed. An inflectionpoint 506 z′ is found to represent the inflection point 506 z that isclosest in proximity to the flange point 500 z, which were bothdiscussed with reference to FIGS. 70A-70D. The point 506 z′ is added toSet A. The flange point 500 z is then projected to a y-z plane. Theresulting flange point 500 z′ is added to Set B.

Thus, Set A contains the following points: the joint spacingcompensation points D1JD2J and P1JP2J and the inflection point 506 z′.Set B contains the following points: Points D1′″D2′″ and P1′″P2′″ (thejoint spacing compensation and silhouette curve data 800 z) and theflange point 500 z′.

vi. Utilize the Data of Sets A and B

Find a rigid body transform. The data points of Set A and Set B arecompared and a rigid body transform that most closely matches Set A toSet B is chosen. The rigid body transform will transform an objectwithout scaling or deforming. That is, the rigid body transform willshow a change of position and orientation of the object. The chosentransform will have rotation about the x-axis and translation in the y-zplane.

Find the inverse of the rigid body transform. The inverse of this rigidbody transform is then imported into the y-z plane that also containsthe femur reference lines D1D2 and P1P2 and the femur spline 508 thatcorresponds to the flange point 500 z of the implant 34′.

The steps described in this Detailed Description are repeated until therelative motion is within a small tolerance. In one embodiment, thesteps are repeated fifty times. In some embodiments, the steps arerepeated more than fifty times or less than fifty times.

In some embodiments, and as shown in FIG. 71A, an acceptable translationin y-z plane may be determined. FIG. 71A depicts an implant that isimproperly aligned on a femur, but shows the range of the search for anacceptable angle A. Within this range for angle A, the translation iny-z leads to finding the rigid body transform as described above. Insome embodiments, the process may optimize y-z translation and rotationaround the x-axis in one step. This can be done by rotating the implantsilhouette curve by several half degree increments and then, for eachincrement, performing the steps described in this Detailed Description.Translation in the y-z axis only occurs during the analysis utilizingthe inverse of the rigid body transform.

vii. Additional Verification and Confirmation of Femur Cut Plane

By using the above outlined procedure, an appropriate implant is foundby choosing the implant and transform combination that provides aninflection angle that is greater than 7 degrees but closest to 7degrees, as explained with reference to FIG. 70A.

In some embodiments, an additional verification step is performed byplacing the implant 34′ in the MRI with the transform 28′″ that is foundby the above described method. As can be understood from FIG. 71B, whichillustrates the implant positioned on the femur transform wherein afemur cut plane is shown, during the verification step, a user maydetermine the amount of bone that is cut J1 on the medial and lateralcondyles by looking at the distal cut plane 514 z of the implant 34′. J1is determined such that the thickness of the bone cut on both the medialand lateral sides is such that the bone is flat after the cut. Multipleslices in both the distal and medial areas of the bone can be viewed toverify J1 is of proper thickness.

Once an appropriate femur implant is chosen, the preoperative planningprocess turns to the selection of an appropriate tibia implant. Thetibia planning process includes a determination of the tibia referencelines to help determine the proper placement of the tibia implant. Thecandidate tibia implant is placed relative to the tibia reference linesand placement is confirmed based on comparison with several 2Dsegmentation splines.

E. Tibia Planning Process

For a discussion of the tibia planning process, reference is now made toFIGS. 72-81D. FIGS. 72-75B illustrate a process in the POP wherein thesystem 10 utilizes 2D imaging slices (e.g., MRI slices, CT slices, etc.)to determine tibia reference data, such as reference points andreference lines, relative to the undamaged side of the tibia plateau.The resulting tibia reference data 900 z is then mapped or projected toan x-y plane (axial plane). A candidate tibia implant is chosen, whichselection will be discussed with reference to FIGS. 76A-76C. The tibiaimplant placement is adjusted and confirmed relative to the tibia, asdiscussed in more detail below with reference to FIGS. 77-81D.

1. Determining Tibia Reference Data

For a discussion of a process used to determine the tibia reference data900 z, reference is now made to FIGS. 72-76B. As can be understood fromFIG. 72, which is a top view of the tibia plateaus 404 z, 406 z of atibia bone image or model 28″, the tibia reference data 900 z mayinclude reference points (e.g. Q1, Q1′), reference lines (e.g. T1T2, V1)and a reference plane (e.g. S′) (see FIGS. 75A-75B). In someembodiments, the tibia reference data 900 z may also include theanterior-posterior extant (tAP) and the medial-lateral extant (tML) ofthe tibia 28″ (see FIGS. 76A-76B). As shown in FIG. 72, each tibiaplateau 404 z, 406 z includes a curved recessed condyle contactingsurface 421 z, 422 z that is generally concave extendinganterior/posterior and medial/lateral. Each curved recessed surface 421z, 422 z is generally oval in shape and includes an anterior curved edge423 z, 424 z and a posterior curved edge 425 z, 426 z that respectivelygenerally define the anterior and posterior boundaries of the condylecontacting surfaces 421 z, 422 z of the tibia plateaus 404 z, 406 z.Depending on the patient, the medial tibia plateau 406 z may have curvededges 424 z, 426 z that are slightly more defined than the curved edges423 z, 425 z of the lateral tibia plateau 404 z.

a. Identify Points Q1, Q2 and Q1′, Q2′

2D slices in the sagittal view are analyzed to determine the tibiaflexion/extension adjustment. Anterior tangent lines TQ1, TQ2 can beextended tangentially to the most anterior location on each anteriorcurved edge 423 z, 424 z to identify the most anterior points Q1, Q2 ofthe anterior curved edges 423 z, 424 z. Posterior tangent lines TQ1′,TQ2′ can be extended tangentially to the most posterior location on eachposterior curved edge 425 z, 426 z to identify the most posterior pointsQ1′, Q2′ of the posterior curved edges 425 z, 426 z. Thus, in oneembodiment, the lateral side tibia plateau 404 z can be analyzed viatangent lines to identify the highest points Q1, Q1′. For example,tangent line TQ1 can be used to identify the anterior highest point Q1,and tangent line TQ1′ can be used to identify the posterior highestpoint Q1′. In some embodiments, a vector V1 extending between thehighest points Q1, Q1′ may be generally perpendicular to the tangentlines TQ1, TQ1′. Similarly, the medial side tibia plateau 406 z can beanalyzed via tangent lines to identify the highest points Q2, Q2′. Forexample, tangent line TQ2 can be used to identify the anterior highestpoint Q2, and tangent line TQ2′ can be used to identify the posteriorhighest point Q2′. In some embodiments, a vector V2 extending betweenthe highest points Q2, Q2′ may be generally perpendicular to the tangentlines TQ2, TQ2′.

i. Confirm points Q1, Q2 and Q1′, Q2′

As can be understood from FIGS. 73A-73D, the location of Q1, Q1′, Q2 andQ2′ may also be confirmed by an analysis of the appropriate sagittalslice. As shown in FIG. 73A, which is a sagittal cross section through alateral tibia plateau 404 z of the tibia model or image 28′, points Q1and Q1′ can be identified as the most anterior and posterior points,respectively, of the curved recessed condyle contacting surface 421 z ofthe lateral tibia plateau 404 z. As shown in FIG. 73B, which is asagittal cross section through a medial tibia plateau 406 z of the tibiamodel 28″, points Q2 and Q2′ can be identified as the most anterior andposterior points, respectively, of the curved recessed condylecontacting surface 422 z of the medial tibia plateau 406 z. Suchanterior and posterior points may correspond to the highest points ofthe anterior and posterior portions of the respective tibia plateaus.

b. Determine lines V1 and V2

As can be understood from FIGS. 72-73B, line V1 extends through anteriorand posterior points Q1, Q1′, and line V2 extends through anterior andposterior points Q2, Q2′. Line V1 is a lateral anterior-posteriorreference line. Line V2 is a medial posterior-anterior reference line.Each line V1, V2 may align with the lowest point of theanterior-posterior extending groove/valley that is the ellipticalrecessed tibia plateau surface 421 z, 422 z. The lowest point of theanterior-posterior extending groove/valley of the elliptical recessedtibia plateau surface 421 z, 422 z may be determined via ellipsoidcalculus. Each line V1, V2 will be generally parallel to theanterior-posterior extending valleys of its respective ellipticalrecessed tibia plateau surface 421 z, 422 z and will be generallyperpendicular to its respective tangent lines TQ1, TQ1′, TQ2, TQ2′. Theanterior-posterior extending valleys of the elliptical recessed tibiaplateau surfaces 421 z, 422 z and the lines V1, V2 aligned therewith maybe generally parallel. The planes associated with lines V1 and V2 aregenerally parallel or nearly parallel to the joint line of the kneejoint, as determined above.

Depending on the patient, the medial tibia plateau 406 z may beundamaged or less damaged than the lateral tibia plateau 404 z. In sucha case, the reference points Q2, Q2′ and reference line V2 of the medialplateau 406 z may be used to establish one or more reference points andthe reference line of the damaged lateral tibia plateau. FIG. 73Cdepicts a sagittal cross section through an undamaged or little damagedmedial tibia plateau 406 z of the tibia model 28″, wherein osteophytes432 z are also shown. As indicated in FIG. 73C, the points Q2, Q2′ canbe located on the undamaged medial plateau and set as reference points.The anterior-posterior reference line, line V2, can be constructed byconnecting the anterior and posterior reference points Q2, Q2′. Thereference line V2 from the undamaged or little damaged medial side issaved for use in determining the reference line of the lateral tibiaplateau in the case where the lateral tibia plateau is damaged. Forexample, as shown in FIG. 73D, which is a sagittal cross section througha damaged lateral tibia plateau 404 z of the tibia model 28″, theanterior point Q1 is found to be undamaged. In this case, theestablished reference line V2 from the medial plateau can be applied tothe damaged lateral plateau by aligning the reference line V2 with pointQ1. By doing so, the reference line V1 of the lateral plateau can beestablished such that line V1 touches the reference point Q1 and extendsthrough the damaged area 403 z. Thus, the reference line V1 in thelateral plateau is aligned to be parallel or nearly parallel to thereference line V2 in the medial plateau. While the above describedprocess is described in terms of extrapolating one or more referencelines of a damaged lateral plateau from an analysis of the undamagedmedial tibia plateau, it is understood that the same process can beundertaken where the lateral tibia plateau is undamaged and one or morereference lines of a damaged medial plateau can be extrapolated from thelateral tibia plateau.

In other embodiments, as can be understood from FIG. 73D and assumingthe damage to the lateral tibia plateau 404 z is not so extensive thatat least one of the highest anterior or posterior points Q1, Q1′ stillexists, the damaged tibia plateau 404 z can be analyzed via tangentlines to identify the surviving high point Q1, Q1′. For example, if thedamage to the lateral tibia plateau 404 z was concentrated in theposterior region such that the posterior highest point Q1′ no longerexisted, the tangent line TQ1 could be used to identify the anteriorhighest point Q1. Similarly, if the damage to the medial tibia plateau406 z was concentrated in the anterior region such that the anteriorhighest point Q1′ no longer existed, the tangent line TQ1′ could be usedto identify the posterior highest point Q1′. In some embodiments, avector extending between the highest points Q1, Q1′ may be generallyperpendicular to the tangent lines TQ1, TQ1′.

c. Determine Reference Points T1 and T2 and Reference Line T1T2

2D slices in both the axial and coronal views are analyzed to determinethe varus/valgus adjustment by finding the reference points T1 and T2.As shown in FIGS. 74A-74B, which are coronal and axial 2D slices of thetibia, reference points T1 and T2 are determined by an analysis of themost proximal coronal slice (FIG. 74A) and the most proximal axial slice(FIG. 74B). As indicated in FIG. 74A, in which the tibia is shown in a0° knee extension, reference points T1 and T2 are determined. The pointsT1 and T2 represent the lowest extremity of tangent contact points oneach of the lateral and medial tibia plateaus, respectively. In oneembodiment, tangent points T1 and T2 are located within the regionbetween the tibia spine and the medial and lateral epicondyle edges ofthe tibia plateau, where the slopes of tangent lines in this region aresteady and constant. For example, and as shown in FIG. 74A, the tangentpoint T1 is in the lateral plateau in Area I between the lateral side ofthe lateral intercondylar tubercle to the attachment of the lateralcollateral ligament. For the medial portion, the tangent point T2 is inArea II between the medial side of the medial intercondylar tubercle tothe medial condyle of the tibia.

As shown in FIG. 74B, the most proximal slice of the tibia in the axialview is analyzed to find reference points T1 and T2. As above, referencepoints T1 and T2 represent the lowest extremity of tangent contactpoints on each of the lateral and medial tibia plateaus. Once thereference points T1 and T2 are found in both the coronal and axialviews, a line T1T2 is found.

A line T1T2 is created by extending a line between reference points T1and T2. In some embodiments, the coronal and axial slices are viewedsimultaneously in order to align the lateral and medialanterior-posterior reference lines V1 and V2. As shown in FIG. 72, thelateral and medial anterior-posterior reference lines V1 and V2 aregenerally perpendicular or nearly perpendicular to line T1T2.

d. Determine the Approximate ACL Attachment Point (AE) and theApproximate PCL Attachment Point (PE) of the Tibia and Reference LineAEPE

As can be understood from FIGS. 72 and 74B, the reference pointsrepresenting the approximate anterior cruciate ligament (ACL) attachmentpoint of the tibia AE and the approximate posterior cruciate ligament(PCL) attachment point of the tibia PE are determined. The referencepoint AE can be determined by finding the approximate tibia attachmentpoint for the ACL. The reference point PE can be determined by findingthe approximate tibia attachment point for the PCL. The line AEPEconnects through reference points AE and PE and may also be referred toas an ACL/PCL bisector line.

e. Confirm Location of Tibia Reference Data

As can be understood from FIG. 72, the tibia reference data 900 zincludes reference points and reference lines that help to defineflexion/extension adjustment, varus/valgus adjustment andinternal/external rotation. For example, the tibia flexion/extensionadjustment is determined by an analysis of the sagittal images as shownin FIGS. 73A-D, which determine reference points Q1, Q1′, Q2, Q2′. Thetibia varus/valgus adjustment may be found by an analysis of FIG. 74Aand finding reference points T1, T2 and reference line T1T2. As can beunderstood from FIG. 72, the proximal reference line, line T1T2, definesthe internal/external rotation as shown in an axial view (line T1T2 inFIG. 74B) and the varus/valgus angle as shown in a coronal view (lineT1T2 in FIG. 74A).

The location of the reference points and reference lines may also beconfirmed based on their spatial relationship to each other. Forexample, as shown in FIGS. 72-73B, the anterior-posterior referencelines V1, V2 of the tibia plateau are generally parallel to the ACL/PCLbisector reference line, line AEPE. As indicated in FIGS. 72 and 74B,the axial-proximal reference line, line T1T2 is perpendicular or nearlyperpendicular to anterior-posterior reference lines V1, V2. As shown inFIG. 72, the tangent lines TQ1, TQ2, TQ1′, TQ2′ are perpendicular ornearly perpendicular to the ACL/PCL bisector reference line, line AEPE.

f. Mapping the Tibia Reference Data to an x-y Plane

As can be understood from FIGS. 75A-75B, which depict the tibiareference data 900 z on a coordinate system (FIG. 75A) and on a proximalend of the tibia (FIG. 75B), the tibia reference data 900 z is mapped toan x-y coordinate system to aid in the selection of an appropriate tibiaimplant. As shown in FIG. 75A, the endpoints Q1, Q1′, Q2, Q2′ and theirrespective anterior posterior reference lines V1 and V2 and theendpoints T1, T2 and the proximal reference line T1T2 are each mapped tothe reference plane. In addition, and as shown in FIG. 75B, thereference data 900 z may be imported onto a 3D model of the tibia 28″for verification purposes. The tibia reference data 900 z is stored forlater analysis.

2. Selecting Tibia Implant Candidate

There are six degrees of freedom for placing the tibial implant onto thetibia. The reference points and reference lines determined above willconstrain all but 2 degrees of freedom which are translated in the x-yplane. The sizing and positioning of the tibia implant (and the femoralcomponent) will be verified with a 2D view of the knee and components.

As briefly discussed above with reference to FIGS. 1A and 50B-50C, whenselecting the tibia implant model 34″ corresponding to the appropriatetibia implant size to be used in the actual arthroplasty procedure, thesystem 4 may use one of at least two approaches to select theappropriate size for a tibia implant [block 115]. In one embodiment, thetibia implant is chosen based on the size of the femoral implant thatwas determined above. In some embodiments, as discussed with referenceto FIGS. 76A-76C, the system 4 determines the tibial anterior-posteriorlength tAP and the tibial medial-lateral length tML and the tibiaimplant 34″ can be selected based on the anterior-posterior extent tAPof the proximal tibia. In some embodiments, the tibia implant may beselected based on both the tibial anterior-posterior length tAP and thetibial medial-lateral length tML.

In one embodiment, there is a limited number of sizes of a candidatetibia implant. For example, one manufacturer may supply six sizes oftibia implants and another manufacturer may supply eight or anothernumber of tibia implants. The anterior-posterior length jAP andmedial-lateral length jML dimensions of these candidate implants may bestored in a database. The tAP and tML are compared to the jAP and jML ofcandidate tibia implants stored in the database.

FIG. 76A is a 2D sagittal image slice of the tibia wherein asegmentation spline with an AP extant is shown. FIG. 76B is an axial endview of the tibia upper end of the tibia bone image or model 28″depicted in FIG. 52A. FIG. 76C is a plan view of the joint side 255 z ofthe tibia implant model 34″ depicted in FIG. 52B. The views depicted inFIGS. 76A-76C are used to select the proper size for the tibial implantmodel 34″. The tibia implant may be chosen based on the maximum tAPextent as measured in an analysis of the segmentation spine as shown inFIG. 76A.

Each patient has tibias that are unique in size and configuration fromthe tibias of other patients. Accordingly, each tibia bone model 28″will be unique in size and configuration to match the size andconfiguration of the tibia medically imaged. As can be understood fromFIG. 76B, the tibial anterior-posterior length tAP is measured from theanterior edge 335 z of the tibial bone model 28″ to the posterior edge330 z of the tibial bone model 28″, and the tibial medial-lateral lengthtML is measured from the medial edge 340 z of the medial plateau of thetibia bone model 28″ to the lateral edge 345 z of the lateral plateau ofthe tibia bone model 28″.

As can be understood from FIG. 76C, each tibial implant available viathe various implant manufacturers may be represented by a specific tibiaimplant 3D computer model 34″ having a size and dimensions specific tothe actual tibia implant. Thus, the representative implant model 34″ ofFIG. 3D may have an associated size and associated dimensions in theform of, for example, anterior-proximal extent tAP and themedial-lateral extent tML of the tibia model 34″, as shown in FIG. 76B.In FIG. 76C, the anterior-posterior extent jAP of the tibia implantmodel 34″ is measured from the anterior edge 315 z to the posterior edge310 z of the tibial implant model 34″, and the medial-lateral extent jMLis measured from the medial edge 320 z to the lateral edge 325 z of thetibial implant model 34″. Once the tibia implant candidate 34″ ischosen, the reference lines jML, jAP of the implant candidate 34″ arestored by the system 4 for later analysis.

3. Determine Tibia Implant Reference Data

As can be understood from FIG. 77, which is a top view of the tibiaplateaus 404 z′, 406 z′ of a tibia implant model 34″, wherein the tibiaimplant reference data 900 z′ is shown, the tibia reference data 900 z′may include tangent points q1, q1′, q2, q2′ and correspondinganterior-posterior reference lines V3, V4 and intersection points t1, t2and its corresponding proximal reference line t1 t 2.

In order to define the implant reference data 900 z′ relative to thetibia model 28″, the implant reference lines jML, jAP are imported intothe same x-y plane with the tibia reference data 900 z that waspreviously mapped to the x-y plane. For gross alignment purposes, themedial-lateral extent jML of the tibia implant 34″ is aligned with theproximal reference line T1T2 of the tibia model 28″. Then, the tibiareference data 900 z′ is determined. The implant 34″ and the bone model28″ may then undergo additional alignment processes.

a. Determine Tangent Points q1, q1′, q2, q2′

As shown in FIG. 77, each tibia plateau 404 z′, 406 z′ includes a curvedrecessed condyle contacting surface 421 z′, 422 z′ that is generallyconcave extending anterior/posterior and medial/lateral. Each curvedrecessed surface 421 z′, 422 z′ is generally oval in shape and includesan anterior curved edge 423 z′, 424 z′ and a posterior curved edge 425z′, 426 z′ that respectively generally define the anterior and posteriorboundaries of the condyle contacting surfaces 421 z′, 422 z′ of thetibia plateaus 404 z′, 406 z′. Thus, the lateral tangent points q1, q1′can be identified as the most anterior and posterior points,respectively, of the curved recessed condyle contacting surface 421 z′of the lateral tibia plateau 404 z′. The medial tangent points q2, q2′can be identified as the most anterior and posterior points,respectively, of the curved recessed condyle contacting surface 422 z′of the medial tibia plateau 406 z′.

b. Determine Reference Lines V3 and V4

As can be understood from FIG. 77, line V3 extends through anterior andposterior points q1, q1′, and line V4 extends through anterior andposterior points q2, q2′. Line V3 is a lateral anterior-posteriorreference line. Line V4 is a medial posterior-anterior reference line.Each line V3, V4 may align with the lowest point of theanterior-posterior extending groove/valley that is the ellipticalrecessed tibia plateau surface 421 z′, 422 z′. The lowest point of theanterior-posterior extending groove/valley of the elliptical recessedtibia plateau surface 421 z′, 422 z′ may be determined via ellipsoidcalculus. Each line V3, V4 will be generally parallel to theanterior-posterior extending valleys of its respective ellipticalrecessed tibia plateau surface 421 z′, 422 z′. The length of thereference lines V3, V4 can be determined and stored for later analysis.

c. Determine Intersection Points t1, t2 and Implant Proximal ReferenceLine t1 t 2

As shown in FIG. 77, the intersection or reference points t1, t2represent the midpoints of the respective surfaces of the lateral tibiaplateau 404 z′ and the medial tibia plateau 406 z′. Also, eachintersection point t1, t2 may represent the most distally recessed pointin the respective tibia plateau 404 z′, 406 z′. An implant proximalreference line t1 t 2 is created by extending a line between the lateraland medial lowest reference points t1, t2. The length of the referenceline t1 t 2 can be determined and stored for later analysis. This linet1 t 2 is parallel or generally parallel to the joint line of the knee.Also, as indicated in FIG. 77, the tibia implant 34″ includes a basemember 780 z for being secured to the proximal tibia 28″.

d. Align Implant Reference Data 900 z′ with Tibia Reference Data 900 z

As can be understood from FIGS. 77 and 75A, the implant reference data900 z′ specifies the position and orientation of the tibia implant 34″and generally aligns with similar data 900 z from the tibia bone model28″. Thus, the lateral tangent points q1, q1′ and medial tangent pointsq2, q2′ of the implant 34″ generally align with the lateral tangentpoints Q1, Q1′ and medial tangent points Q2, Q2′ of the tibia 28″. Theanterior posterior reference lines V3, V4 of the implant 34″ generallyalign with the anterior posterior reference lines V1, V2 of the tibiamodel 28″. The intersection points t1, t2 of the implant 34″ generallyalign with the reference points T1, T2 of the tibia 28″. The proximalreference line t1 t 2 of the implant 34″ generally aligns with theproximal reference line T1T2 of the tibia 28″. Reference line t1 t 2 isapproximately perpendicular to the anterior-posterior reference linesV3, V4.

The implant reference data 900 z′ lies on a coordinate frame, plane r′.The tibia reference data 900 z lies on a coordinate frame, plane s′.Thus, the alignment of the implant 34″ with the tibia 28″ is thetransformation between the two coordinate frames plane r′, plane s′.Thus, the gross alignment includes aligning the proximal line t1 t 2 ofthe implant 34″ to the proximal line T1T2 of the tibia 28″. Then, in afurther alignment process, the reference points t1, t2 of the implantand the reference points T1, T2 of the tibia 28″ are aligned. Theimplant 34″ is rotated such that the sagittal lines of the implant 34″(e.g. V3, V4) are parallel or generally parallel to the sagittal linesof the tibia 28″ (e.g. V1, V2). Once the tibia 28″ and the implant 34″are in alignment (via the reference data 900 z, 900 z′), the tibial cutplane can be determined.

4. Determine Surgical Cut Plane for Tibia

a. Determine Cut Plane of the Tibia Implant

The cut plane of the tibia implant is determined. The user may determinethis cut plane by a method such as one described with respect to FIGS.78A-78C. FIG. 78A is an isometric view of the 3D tibia bone model 1002 zshowing the surgical cut plane SCP design. FIGS. 78B and 78C aresagittal MRI views of the surgical tibia cut plane SCP design with theposterior cruciate ligament PCL.

During the TKA surgery, the damaged bone surface portions of theproximal tibia will be resected from the cut plane level 850 z and beremoved by the surgeon. As shown in FIGS. 78B and 78C, the surgicaltibial cut plane 850 z may be positioned above the surface where the PCLis attached, thereby providing for the maintenance of the PCL during TKAsurgery.

FIG. 79A is an isometric view of the tibia implant 34″ wherein a cutplane r1 is shown. As can be understood from FIG. 79A, the cut plane r1of the implant 34″ is the surgical tibial cut plane 850 z and is a datapoint or set of data points that may be stored in the implant database.In order to determine whether an adjustment to the cut plane r1 must bemade, the cut plane r1 of the tibia implant 34″ is aligned with thetibia 28″.

b. Determine Initial Cut Plane of the Tibia

As shown in FIG. 79B, which is a top axial view of the implant 34″superimposed on the tibia reference data 900 z, the implant 34″ isopened with the tibia reference data 900 z and is generally aligned withthe tibia reference data 900 z at the level of the cut plane r1 by thesystem 4. However, the implant 34″ is not centered relative to the tibiareference data 900 z. The anterior/posterior extent tAP″ andmedial/lateral extent tML″ of the tibia 28″ at the cut level are found.

The implant 34″ may be centered by the system (or manually by a user ofthe system). As indicated in FIG. 79C, which is an axial view of thetibial implant aligned with the tibia reference data 900 z, the tibiaimplant 34″ is then centered relative to the anterior posterior extenttAP″ and the medial lateral extents tML″ of the tibia 28″.

c. Determine Joint Line and Adjustment

In order to allow an actual physical arthroplasty implant to restore thepatient's knee to the knee's pre-degenerated or natural configurationwith the its natural alignment and natural tensioning in the ligaments,the condylar surfaces of the actual physical implant generally replicatethe condylar surfaces of the pre-degenerated joint bone. In oneembodiment of the systems and methods disclosed herein, condylarsurfaces of the bone model 28″ are surface matched to the condylarsurfaces of the implant model 34″. However, because the bone model 28″may be bone only and not reflect the presence of the cartilage thatactually extends over the pre-degenerated condylar surfaces, the surfacematching of the modeled condylar surfaces may be adjusted to account forcartilage or proper spacing between the condylar surfaces of thecooperating actual physical implants (e.g., an actual physical femoralimplant and an actual physical tibia implant) used to restore the jointsuch that the actual physical condylar surfaces of the actual physicalcooperating implants will generally contact and interact in a mannersubstantially similar to the way the cartilage covered condylar surfacesof the pre-degenerated femur and tibia contacted and interacted.

i. Determine Adjustment Value tr

Thus, in one embodiment, the implant model is modified or positionallyadjusted (via e.g. the tibia cut plane) to achieve the proper spacingbetween the femur and tibia implants. To achieve the correct adjustmentor joint spacing compensation, an adjustment value tr may be determined.In one embodiment, the adjustment value tr that is used to adjust theimplant location may be based off of an analysis associated withcartilage thickness. In another embodiment, the adjustment value tr usedto adjust the implant location may be based off of an analysis of properjoint gap spacing, as described above with respect to FIGS. 63G and 63H.Both of the methods are discussed below in turn.

1. Determining Cartilage Thickness

FIG. 79D is a MRI image slice of the medial portion of the proximaltibia and indicates the establishment of landmarks for the tibia POPdesign. FIG. 79E is a MRI image slice of the lateral portion of theproximal tibia. The wm in FIG. 79D represents the cartilage thickness ofthe medial tibia meniscus, and the wl in FIG. 79E represents thecartilage thickness of the lateral tibia meniscus. In one embodiment,the cartilage thicknesses wl and wm are measured for the tibia meniscusfor both the lateral and medial plateaus 760 z, 765 z via the MRI slicesdepicted in FIGS. 79D and 79E. The measured thicknesses may be compared.If the cartilage loss is observed for the medial plateau 765 z, then thewlmin of lateral plateau 760 z is selected as the minimum cartilagethickness. Similarly, if the lateral plateau 760 z is damaged due tocartilage loss, then the wmmin of medial plateau 765 z is selected asthe minimum cartilage thickness. The minimum cartilage wr may beillustrated in the formula, wr=min (wm, wl). In one embodiment, forpurposes of the adjustment to the tibia, the adjustment value tr may bemay be equal to the minimum cartilage value wr.

2. Determining Joint Gap

In one embodiment, the joint gap is analyzed as discussed above withrespect to FIGS. 63G and 63H to determine the restored joint line gapGp3. In one embodiment, for purposes of the adjustment to the tibiashape matching, the adjustment value tr may be calculated as being halfof the value for Gp3, or in other words, tr=Gp3/2.

d. Determine Compensation for Joint Spacing

After centering the implant 34″ within the cut plane, joint spacingcompensation is taken into account. As shown in FIG. 79F, which is anisometric view of the tibia implant and the cut plane, the implant 34″and cut plane-r1 are moved in a direction that is generallyperpendicular to both the proximal and sagittal reference lines by anamount equal to adjustment value tr, thereby creating an adjusted cutplane r1′. In one embodiment, the adjustment value tr is equal toapproximately one-half of the joint spacing. In other embodiments, theadjustment value tr is equal to the cartilage thickness.

Thus, the implant candidate may be selected relative to the jointspacing compensation that was determined previously with reference toFIGS. 63G, 63H, 79D and 79E. As discussed above, in one embodiment, oncethe joint spacing compensation is determined, one-half of the jointspacing compensation will be factored in to the femur planning processand one-half of the joint spacing compensation will be factored in tothe tibia planning process. That is, the femur implant is adjusted by anamount equal to one-half of the joint spacing compensation. Thus, thecandidate femur implant will be chosen such that when it is positionedon the femur relative to the joint spacing compensation, the candidateimplant will approximate the pre-degenerated joint line. Similarly, thetibia implant is adjusted by an amount equal to one-half of the jointspacing compensation. Thus, the candidate tibia implant will be chosensuch that when it is positioned on the tibia relative to the jointspacing compensation, the candidate implant will approximate thepre-degenerated joint line. Also, the tibia implant mounting post 780 z(see FIG. 80B) and the femur implant mounting post 781 z (see FIG. 31A)will be oriented at approximately the center of the tibia and femur.

F. Verification of Implant Planning Models and Generation of SurgicalJigs Based on Planning Model Information

FIGS. 80A-81 illustrate one embodiment of a verification process thatmay be utilized for the preoperative planning process disclosed herein.FIGS. 80A-80C are sagittal views of a 2D image slice of the femur 28′(FIGS. 80A and 80B) and the tibia 28″ (FIG. 80B) wherein the 2D computergenerated implant models 34 are also shown. As can be understood fromFIGS. 80A-80C, verification for both the distal femur and proximal tibiais performed by checking the reference lines/planes in 2D sagittalviews. The reference lines/planes may also be checked in other views(e.g. coronal or axial). For example, and as can be understood fromFIGS. 80A and 80B, for the femur planning model, the flexion-extensionrotation is verified by checking whether the inflection point 506 z ofthe anterior cortex of the femur 28′ sufficiently contacts the interiorsurface 510 z of the anterior flange 512 z of implant 34′. That is, ascan be understood from FIG. 80A2, when the implant 34′ is properlyaligned with the femur 28′, the flange point 500 z of the implant shouldtouch the inflection point of the segmentation spline or femur 28′.

As can be understood with reference to FIG. 80B, the tibia planning maybe verified by looking at a 2D sagittal slice. Depending on the initialplanning choice made above, one of the following can be verified: 1)whether the size of the tibial implant 34″ matches or corresponds withthe size of the femoral implant 34′, or 2) whether the tibial implant34″ is one size larger or one size smaller than the femoral implant 34′size (e.g., a size 2 femur, and a size 1 tibia; or a size 2 femur, and asize 2 tibia; or a size 2 femur, and a size 3 tibia). In otherembodiments, the size of tibial implant may be chosen without takinginto account the size of the femoral implant. One of skill in the artwill recognize that different implant manufacturers may utilize adifferent naming convention to describe different sizes of implants. Theexamples provided herein are provided for illustrative purposes and arenot intended to be limiting.

As indicated in FIG. 80B, the placement of the tibial implant can beverified by viewing the anterior and posterior positions of the implant34″ relative to the tibial bone 28″. If the implant is properlypositioned, the implant should not extend beyond the posterior oranterior edge of the tibia bone. The flexion-extension of the tibia 28″can be verified by checking that the tibial implant reference line 906z, which is a line segment approximating the normal direction of theimplant's proximal surface, is at least parallel with the posteriorsurface 904 z of the tibia 28″ or converging with the posterior tibialsurface 906 z around the distal terminus of the tibial shaft.

In other embodiments, as shown in FIGS. 81A-81G and FIGS. 82A-82C, theplanning can also be confirmed from generated 3D bone models 1000 z,1002 z and 3D implant models 1004 z, 1006 z. If the planning is doneincorrectly, the reference lines 100 z, 100 z′, 900 z, 900 z′ will becorrected in the 2D MRI views to amend the planning. FIGS. 81A-81C andFIGS. 81E-81G are various views of the implant 3D models 1004 z, 1006 zsuperimposed on the 3D bone models 1000 z, 1002 z. FIG. 81D is a coronalview of the bone models 1000 z, 1002 z.

FIGS. 81A-81G show an embodiment of the POP system disclosed herein. Thealignment of the implant models 1004 z, 1006 z with the bone models 1000z, 1002 z is checked in the anterior view (FIG. 81A), the posterior view(FIG. 81E), the lateral view (FIG. 81B), the medial view (FIG. 81C), thetop view (FIG. 81F) and the bottom view (FIG. 81G).

The flexion/extension between the femur and tibia implant models 1004 z,1006 z and the femur and tibia bone models 1000 z, 1002 z is examined inboth the medial view and the lateral view. For example, FIG. 81B showsthe lateral view wherein the knee is shown in full extension or 0 degreeflexion and in its natural alignment similar to its pre-arthritis status(e.g., neutral, varus or valgus), and FIG. 81C shows the medial view ofthe knee in full extension or 0 degree flexion and in its naturalalignment (e.g., neutral, varus or valgus).

FIG. 81D shows the varus/valgus alignment of the knee model 28 m′, 28 m″with the absence of the implants 34 m′, 34 m″. The gaps Gp4, Gp5 betweenthe lowermost portions of distal femoral condyles 302 z, 303 z and thelowermost portions of the tibia plateau 404 z, 406 z will be measured inthe femoral and tibia bone models 28 m′, 28 m″. Gap Gp4 represents thedistance between the distal lateral femoral condyle 302 z and thelateral tibial plateau 404 z. Gap Gp5 represents the distance betweenthe distal medial femoral condyle 303 z and the medial tibial plateau406 z. In the varus/valgus rotation and alignment, Gp4 is substantiallyequal to Gp5, or |Gp4-Gp5|<<1 mm. FIG. 81D shows the knee model 28 m′,28 m″ that is intended to restore the patient's knee back to his pre-OAstage.

The IR/ER rotation between the femur and tibia implant models 1004 z,1006 z and the femur and tibia bone models 1000 z, 1002 z is examined inboth the top and bottom views. For example, FIG. 81F shows the top viewof the tibia showing the IR/ER rotation between no flexion and highflexion, and FIG. 81G shows the bottom view of the femur showing theIR/ER rotation between no flexion and high flexion. The stem of thetibia implant model 1006 z and the surgical cut plane of the tibiaimplant model 1006 z provide the information for the IR/ER rotation.

FIGS. 82A-82C show another embodiment of the POP system disclosedherein.

FIG. 82A is an medial view of the 3D bone models. FIG. 82B is an medialview of the 3D implant models. FIG. 82C is an medial view of the 3Dimplant models superimposed on the 3D bone models.

As shown in FIG. 82A, a 3D model of the femur bone 1000 z and a 3D modelof the tibia bone 1002 z may be generated from the 2D segmentationsplines of image slices and the reference data 100 z, 900 z determinedabove for verification of the POP. As shown in FIG. 82B, a 3D model ofthe femur implant 1004 z and a 3D model of the tibia implant 1006 z maybe generated based on the reference lines 100 z′, 900 z′ determinedabove for verification of the POP. The implant models 1004 z, 1006 z andthe bone models 1000 z, 1002 z are aligned based on the reference linesin a 3D computer modeling environment and the alignment is checked inthe sagittal view as shown in FIG. 82C. If the alignment of the bonemodels 1000 z, 1002 z and the implant models 1004 z, 1006 z is notcorrect, the reference lines 100 z, 100 z′, 900 z, 900 z′ will becorrected in the 2D views to amend the planning.

The knee model 28′, 28″, 1000 z, 1002 z and associated implant models34′, 34″, 1004 z, 1006 z developed through the above-discussed processesinclude dimensions, features and orientations that the system 10depicted in FIG. 1A can be utilized to generate 3D models of femur andtibia cutting jigs 2. The 3D model information regarding the cuttingjigs can then be provided to a CNC machine 10 to machine the jigs 2 froma polymer or other material.

G. Mechanical Axis Alignment

While much of the preceding disclosure is provided in the context ofachieving natural alignment for the patient's knee post implantation ofthe actual physical femur and tibia implants, it should be noted thatthe systems and methods disclosed herein can be readily modified toproduce an arthroplasty jig 2 that would achieve a zero degreemechanical axis alignment for the patient's knee post implantation.

For example, in one embodiment, the surgeon utilizes a natural alignmentfemoral arthroplasty jig 2A as depicted in FIGS. 51A and 51B to completethe first distal resection in the patient's femoral condylar region.Instead of utilizing a natural alignment tibia arthroplasty jig 2B asdepicted in FIGS. 51C and 51D, the surgeon instead completes the firstproximal resection in the patient's tibia plateau region via free handor a mechanical axis guide to cause the patient's tibia implant toresult in a mechanical axis alignment or an alignment based off of themechanical axis (e.g., an alignment that is approximately one toapproximately three degrees varus or valgus relative to zero degreemechanical axis).

In one embodiment of the POP systems and methods disclosed herein,instead of superposing the 3D bone models 1000 z, 1002 z to the 3Dimplant models 1004 z, 1006 z in a manner that results in the saw cutand drill hole data 44 that leads to the production of natural alignmentarthroplasty jigs 2A, 2B, the superposing of the bone and implant models1000 z, 1002 z, 1004 z, 1006 z may be conducted such that the resultingsaw cut and drill hole data 44 leads to the production of zero degreemechanical axis alignment arthroplasty jigs or some other type ofarthroplasty jig deviating in a desired manner from zero degreemechanical axis.

Thus, depending on the type of arthroplasty jig desired, the systems andmethods disclosed herein may be applied to both the production ofnatural alignment arthroplasty jigs, zero degree mechanical axisalignment jigs, or arthroplasty jigs configured to provide a result thatis somewhere between natural alignment and zero degree mechanical axisalignment.

Although the present invention has been described with respect toparticular embodiments, it should be understood that changes to thedescribed embodiments and/or methods may be made yet still embraced byalternative embodiments of the invention. For example, certainembodiments may operate in conjunction with a MRI or a CT medicalimaging system. Yet other embodiments may omit or add operations to themethods and processes disclosed herein. Accordingly, the proper scope ofthe present invention is defined by the claims herein.

What is claimed is:
 1. A computer-implemented method of preoperativelyplanning a surgical procedure on a knee of a patient, the knee joiningtogether a femur having femoral condyles and a tibia having a tibialplateau, the surgical procedure comprising implanting an implant on atleast one of the femur and the tibia as part of the procedure, themethod comprising: determining femoral condyle vectors and tibialplateau vectors based on image data of the knee, the femoral condylevectors and the tibial plateau vectors corresponding to motion vectorsof the femoral condyles and the tibial plateau as they move relative toeach other; modifying a bone model representative of at least one of thefemur and the tibia into a modified bone model based on the femoralcondyle vectors and the tibial plateau vectors; and determiningcoordinate locations for a resection of the modified bone model.
 2. Themethod of claim 1, wherein modifying the bone model comprises modifyinga shape of femoral condyles of the bone model.
 3. The method of claim 1,wherein modifying the bone model comprises modifying a shape of a tibialplateau of the bone model.
 4. The method of claim 1, wherein modifyingthe bone model comprises restoring a surface of the bone model to a lessdegenerated state.
 5. The method of claim 1, wherein the bone model is afemoral bone model and a tibial bone model.
 6. The method of claim 1,wherein the modified bone model includes a modification to a surfaceprofile of the bone model.
 7. The method of claim 6, wherein themodified bone model is a restored bone model with the surface profilebeing modified from a degenerated state to a less degenerated state. 8.The method of claim 1, wherein modifying the bone model into a modifiedbone model comprises: determining reference information from a referenceportion of the bone model representative of the at least one of thefemur and the tibia in a degenerated state; and using the referenceinformation to restore a degenerated portion of the bone model into arestored portion representative of the degenerated portion in a lessdegenerated state.
 9. The method of claim 1, wherein the image data ofthe knee comprises two dimensional image views of the knee, and thefemoral condyle vectors and tibial plateau vectors are determined basedon an analysis of geometric features of the femoral condyles and tibialplateau in the two dimensional image views of the knee.
 10. The methodof claim 1, wherein determining coordinate locations for a resection ofthe modified bone model comprises: aligning points on an implant modelwith corresponding points on the modified bone model, wherein theimplant model is positioned and oriented relative to the modified bonemodel in a coordinate system that is reflective of the implant beingimplanted on the femur.
 11. The method of claim 10, wherein the pointson the modified bone model comprise a first point at a most distallocation on a condylar surface of the modified bone model and a secondpoint on a location on the condylar surface of the modified bone modelthat is proximal to the first point.
 12. The method of claim 10, whereinthe points on the implant model comprise a third point at a most distallocation on a condylar surface of the implant model and a fourth pointon a location on the condylar surface of the implant model that isproximal to the third point.
 13. The method of claim 1, whereindetermining coordinate locations for a resection of the modified bonemodel comprises: aligning a point on an implant model with acorresponding point on the modified bone model, wherein the implantmodel is positioned and oriented relative to the modified bone model ina coordinate system that is reflective of the implant being implanted onthe tibia.
 14. The method of claim 13, wherein the point on the modifiedbone model comprise a first point at a most distally recessed locationon a condylar surface of the modified bone model.
 15. The method ofclaim 13, wherein the point on the implant model comprises a secondpoint at a most distally recessed location on a condylar surface of theimplant model.
 16. The method of claim 1, wherein the image data of theknee comprises preoperatively generated medical images.
 17. The methodof claim 1, wherein determining coordinate locations for a resection ofthe modified bone model comprises automatically identifying apreliminary position and orientation of the resection.
 18. A method ofplanning and performing a surgical procedure on a knee of a patient, theknee joining together a femur having femoral condyles and a tibia havinga tibial plateau, the method comprising: performing a motion analysis ofthe knee, whereby a 3D bone model representing at least one of the femurand tibia is modified into a modified 3D bone model based on the motionanalysis of the knee; and determining coordinate locations for aresection of the modified bone model.
 19. The method of 18, whereinperforming the motion analysis of the knee comprises using a computer todetermine femoral condyle vectors and tibial plateau vectorscorresponding to motion vectors of the femoral condyles and the tibialplateau as they move relative to each other.
 20. A method ofmanufacturing a custom arthroplasty guide, the guide including a matingregion configured to matingly receive a portion of a patient boneassociated with an arthroplasty procedure for which the customarthroplasty guide is to be employed, the mating region including asurface contour that is generally a negative of a surface contour of theportion of the patient bone, the surface contour of the mating regionbeing configured to mate with the surface contour of the portion of thepatient bone in a generally matching or interdigitating manner when theportion of the patient bone is matingly received by the mating region,the method of manufacture comprising: a) generating medical imaging dataof the portion of the patient bone; b) identifying landmarks associatedwith bone boundaries in the medical imaging data; c) providing ageometrical representation of an exemplary bone that is not the patientbone but is the same bone type as the patient bone and is representativeof what is considered to be generally normal with respect to the bonetype for at least one of size, condition, shape, weight, age, height,race, gender or diagnosed disease condition; d) adjusting thegeometrical representation of the exemplary bone to match the landmarks;e) using the adjusted geometrical representation of the exemplary boneto generate a three dimensional computer model of the portion of thepatient bone; f) using the three dimensional computer model to generatedesign data associated with the custom arthroplasty guide; and g) usingthe design data in manufacturing the custom arthroplasty guide.